Initial commit

.gitattributes

@@ -0,0 +1 @@
+results/compiled.pkl filter=lfs diff=lfs merge=lfs -text

.github/workflows/hf_leaderboard_deployment.yml

@@ -0,0 +1,25 @@
+name: HF Leaderboard Deployement
+
+on:
+  workflow_run:
+    workflows: ["Test and Compile"]
+    branches: [main]
+    types: 
+      - completed
+
+permissions:
+  contents: read
+
+jobs:
+  sync-to-hub: 
+    runs-on: ubuntu-latest
+    if: ${{ github.event.workflow_run.conclusion == 'success' }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to huggingface main leaderboard
+        env:
+          HF_TOKEN: ${{ secrets.MAIN_EXT_HF_GEOBENCH }}
+        run: git push https://test-gb:$HF_TOKEN@huggingface.co/spaces/aialliance/GEO-Bench-2-Leaderboard main

.github/workflows/test_and_compile.yml

@@ -0,0 +1,76 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Test and Compile
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  build-and-test: 
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest pytest-cov
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest -s --cov=. tests/
+
+  validate-input-and-recompute: 
+    needs: build-and-test
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8 pytest pytest-cov
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      - name: Validate new submissions
+        run: python -m utils.input_validation
+      - name: Compile all results
+        run: python -m utils.compile_results
+      - name: Add results
+        run: |
+          git config --global user.name 'naomi-simumba'
+          git config --global user.email 'naomi-simumba@users.noreply.github.com'
+          git add .
+          git commit -m "auto update"
+          git push
+
+
+
+  check-file-size: #HF has a  10MB filesize limit
+    needs: build-and-test
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/lfs-warning@v2.0
+        with:
+          filesizelimit: 10485760 

.gitignore

@@ -0,0 +1,2 @@
+utils/__pycache__/
+.DS_Store

Dockerfile

@@ -0,0 +1,14 @@
+FROM python:3.10
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+COPY ./app.py /code/app.py
+COPY ./results /code/results
+COPY ./utils /code/utils
+
+
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+CMD ["streamlit", "run", "/code/app.py", "--server.address", "0.0.0.0", "--server.port", "7860"]

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -0,0 +1,76 @@
+---
+title: GEO-Bench Leaderboard
+emoji: 🏆
+colorFrom: purple
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+# 🏆 GEO-Bench Leaderboard
+
+The [GEO-Bench leaderboard](https://huggingface.co/spaces/aialliance/GEO-Bench-Leaderboard) tracks performance of geospatial foundation models on various benchmark datasets using the GEO-Bench benchmarking framework. 
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Language: Python](https://img.shields.io/badge/language-Python%203.10%2B-green?logo=python&logoColor=green)](https://www.python.org)
+
+## 1. How to Submit New Results
+
+### 1.1. Create New Submission Directory
+Create a new folder in the `new_submission` top directory:
+```bash
+geobench_leaderboard/
+└── new_submission/
+    ├── results_and_parameters.csv
+    ├── additional_info.json
+```
+
+### 1.2. Add Results and Parameters Details
+Add a CSV file (`results_and_parameters.csv`) with the columns below. Please note that if terratorch-iterate is used for experiments, this table may be created automatically upon completion of an experiment. Please see the `examples/results_and_parameters.csv`  for an example.
+ - `backbone`: backbone used for experiment, (e.g. Prithvi-EO-V2 600M)
+ - `dataset`: some or all of the GEO-bench datasets. Please see Info page to learn more.
+ - `Metric`: the type of metric used for evaluation. Depending on the dataset, this may be one of the following: `Overall_Accuracy`, `Multilabel_F1_Score`, `Multiclass_Jaccard_Index`
+ - `experiment_name`: if terratorch-iterate used, this will the experiment_name used in mlflow. Otherwise, a unique name may be used for all results relating to a single backbone
+ - `batch_size_selection`: denotes whether the batch size was fixed during hyperparameter optimization. May be `fixed` or `optimized`
+ - `early_stop_patience`: early stopping patience using for trainer
+ - `n_trials`: number of trials used for hyperparameter optimization
+ - `Seed`: random seed used for repeated experiment. At least 5 random seeds must be used for each backbone
+ - `batch_size`: batch size used for repeated experiments for each backbone/dataset combination.
+ - `weight_decay`: weight decay experiments for each backbone/dataset combination.
+ - `lr`: learning rate used for repeated experiments for each backbone/dataset combination. Obtained from hyperparameter optimization (HPO)
+ - `test metric`: metric obtained from running backbone on the dataset during repeated experiment. Please see Info page to learn more. 
+
+
+### 1.3. Add Additional Information
+Create a JSON file (`additional_info.json`) with information about your submission and any new models that will be included.
+The JSON file MUST have the same file name and contain the same keys as the `examples/additional_info.json` file. 
+
+
+### 1.4. Submit PR
+
+ - Fork the repository
+ - Add your results following the structure above and in the PR comments add more details about your submission
+ - Create a pull request to main
+
+
+## 2. Benchmarking with Terratorch-Iterate
+The [TerraTorch-Iterate](https://github.com/IBM/terratorch-iterate) library, based on [TerraTorch](https://github.com/IBM/terratorch), leverages MLFlow for experiment logging, optuna for hyperparameter optimization and ray for parallelization. It includes functionality to easily perform both hyperparameter tuning and re-repeated experiments in the manner prescribed by the GEO-Bench protocol. The `summarize` feature of `TerraTorch-Iterate` can be used to automatically create a `results_and_parameters.csv` file for submission, once benchmarking is complete.
+
+### 2.1 Installation
+Please see [TerraTorch-Iterate](https://github.com/IBM/terratorch-iterate) for installation instructions
+
+### 2.2 Running benchmark experiments
+**On existing models**:  To run experiments on an existing model, a custom config file specifying the model and dataset parameters should be prepared. To compare performance of multiple models, define a config file with unique experiment name for each model being comapred. Please see the `examples` folder for sample config files. Each config file (experiment) can then be executed with the following command:
+
+```
+terratorch iterate --hpo --repeat --config <config-file>
+```
+
+**On new models**: New models can be evaluated by first onboarding them to the [TerraTorch](https://github.com/IBM/terratorch/) library. Once onboarded, benchmarking may be conducted as outlined above.
+
+
+### 2.3 Summarizing and plotting results 
+**Extract results and parameters**: The command below can be used to extract results and hyperparameters file for submission to the lederboard. Please see details at the following link: https://github.com/terrastackai/iterate?tab=readme-ov-file#summarizing-results. 
+```
+terratorch iterate --summarize --config <summarize-config-file>
+```

app.py

@@ -0,0 +1,559 @@
+import os
+import json
+import re
+import streamlit as st
+import pandas as pd
+import numpy as np
+from urllib.parse import quote
+from pathlib import Path
+import re
+import html
+import pickle
+from typing import Dict, Any, Tuple
+from scipy.stats import sem
+from utils.constants import (DATASETS, DIGITS_FOR_VALUES, DIGITS_FOR_ERRORS,
+                               DATASET_INFO, DIMENSIONS, RESULTS_DIR,
+                               DIMENSION_INFO)
+
+
+def sanitize_model_name(model_name):
+    # Only allow alphanumeric chars, hyphen, underscore
+    if model_name.startswith('.'):
+        raise ValueError("model name cannot start with a dot")
+    
+    if not re.match("^[a-zA-Z0-9-_][a-zA-Z0-9-_.]*$", model_name):
+        raise ValueError("Invalid model name format")
+    return model_name
+
+
+def safe_path_join(*parts):
+    # Ensure we stay within results directory
+    base = Path("results").resolve()
+    try:
+        path = base.joinpath(*parts).resolve()
+        if not str(path).startswith(str(base)):
+            raise ValueError("Path traversal detected")
+        return path
+    except Exception:
+        raise ValueError("Invalid path")
+
+
+def sanitize_column_name(col: str) -> str:
+    """Sanitize column names for HTML display"""
+    col= str(col)
+    is_result_column = [True if item in col else False for item in ["IQM", "Mean"]]    
+    col = col.replace("_", " ") if any(is_result_column) else col.replace("_", " ").title()
+    return html.escape(col)
+
+
+
+def sanitize_cell_value(value: Any) -> str:
+    """Sanitize cell values for HTML display"""
+    if value == "nan ± nan":
+        value = "NA"
+    if isinstance(value, (int, float)):
+        output = str(value)
+    else:
+        output = html.escape(str(value))
+    return output
+
+
+def heat_rgb01(t: float, max_val: int) -> Tuple[int, int, int]:
+    """Map t in [0,1] to white → IBM blue (#0F62FE)."""
+    min_val = 0
+    t = max(min_val, min(1, (t/max_val)))
+    # t = (t - min_val)/max_val
+    # White RGB
+    wr, wg, wb = 255, 255, 255
+    # IBM blue RGB
+    br, bg, bb = 15, 98, 254
+    r = int(round(wr + (br - wr) * t))
+    g = int(round(wg + (bg - wg) * t))
+    b = int(round(wb + (bb - wb) * t))
+    return r, g, b
+
+def rgb_to_css(rgb: Tuple[int, int, int]) -> str:
+    r, g, b = rgb
+    return f"rgb({r}, {g}, {b})"
+
+def readable_text_color(rgb: Tuple[int, int, int]) -> str:
+    """Pick text color (#222 or white) based on perceived luminance."""
+    r, g, b = rgb
+    y = 0.2126 * r + 0.7152 * g + 0.0722 * b
+    return "#222222" if y > 160 else "#ffffff"
+
+
+def create_html_results_table(df, df_err, rank_table, display_rank=False):
+    html = '''
+    <style>
+        table {
+            width: 100%;
+            border-collapse: collapse;
+        }
+        th, td {
+            border: 1px solid #ddd;
+            padding: 8px;
+            text-align: center;
+        }
+        th {
+            font-weight: bold;
+        }
+        .table-container {
+            padding-bottom: 20px;
+        }
+    </style>
+    '''
+    html += '<div class="table-container">'
+    html += '<table>'
+    html += '<thead><tr>'
+    
+    rank_index = list(range(1, df.shape[0]+1))
+    df.insert(loc=0, column='rank', value=rank_index)
+
+    columns = list(df.columns)
+    columns = [column for column in df.columns if column != "index"]
+    for column in columns:
+        html += f'<th>{sanitize_column_name(column)}</th>'
+    html += '</tr></thead>'
+    html += '<tbody>'
+
+    for (_, row), (_, row_err), (_, rank_row) in zip(df.iterrows(), df_err.iterrows(), rank_table.iterrows()):
+        html += '<tr>'
+        for col in columns:
+            #if column == "index": continue
+            if col == "Model":
+                html += f'<td>{row[col]}</td>'
+            else:
+                if col in row_err:
+                    if row[col] != row_err[col]:
+                        rgb = heat_rgb01(rank_row[col], rank_index[-1])
+                        bg = rgb_to_css(rgb)
+                        fg = readable_text_color(rgb)
+                        if display_rank:
+                            display_val = row[col] if np.isnan(row[col]) else int(rank_row[col])
+                            new_val = f'<td style="background-color:{bg};color:{fg}">{sanitize_cell_value(display_val)}</td>'
+                        else:
+                            new_val = f'<td style="background-color:{bg};color:{fg}">{sanitize_cell_value(row[col])} ± {sanitize_cell_value(row_err[col])} </td>'
+                        html += new_val
+                    else:
+                        html += f'<td>{sanitize_cell_value(row[col])}</td>'
+                else:
+                    html += f'<td>{sanitize_cell_value(row[col])}</td>'
+                    
+        html += '</tr>'
+    html += '</tbody></table>'
+    html += '</div>'
+    return html
+
+def create_html_table_info(df):
+    #create html table
+    html = '''
+    <style>
+        table {
+            width: 100%;
+            border-collapse: collapse;
+        }
+        th, td {
+            border: 1px solid #ddd;
+            padding: 8px;
+            text-align: center;
+        }
+        th {
+            font-weight: bold;
+        }
+        .table-container {
+            padding-bottom: 20px;
+        }
+    </style>
+    '''
+    html += '<div class="table-container">'
+    html += '<table>'
+    html += '<thead><tr>'
+    for column in df.columns:
+        html += f'<th>{sanitize_column_name(column)}</th>'
+    html += '</tr></thead>'
+    html += '<tbody>'
+    
+    for (_, row) in df.iterrows():
+        html += '<tr>'
+        for column in df.columns:
+            if column == "Citation":
+                html += f'<td>{row[column]}</td>'
+            else:
+                html += f'<td>{sanitize_cell_value(row[column])}</td>'
+        html += '</tr>'
+    html += '</tbody></table>'
+    html += '</div>'
+    return html
+    
+
+def check_sanity(model_name):
+    try:
+        safe_model = sanitize_model_name(model_name)
+        for benchmark in DATASETS:
+            file_path = safe_path_join(safe_model, f"{benchmark.lower()}.json")
+            if not file_path.is_file():
+                continue
+            original_count = 0
+            with open(file_path) as f:
+                results = json.load(f)
+                for result in results:
+                    if result["original_or_reproduced"] == "Original":
+                        original_count += 1
+            if original_count != 1:
+                return False
+        return True
+    except ValueError:
+        return False
+
+                
+def make_hyperlink_datasets(url: str ,
+                            url_name: str,
+                            root: str = "") -> str:
+    try:
+        if len(url) == 0:
+            return url_name
+        full_url = f"{root}{url}"
+        return f'<a href="{html.escape(full_url)}" target="_blank">{html.escape(url_name)}</a>'
+    except ValueError:
+        return ""
+                
+
+        
+def filter_with_user_selections(unique_key: str,
+                                iqm_column_name: str,
+                                table = pd.DataFrame,
+                                table_err = pd.DataFrame
+                                ) -> tuple[pd.DataFrame, pd.DataFrame]:
+    
+    table.reset_index(inplace=True)
+    table_err.reset_index(inplace=True)
+    #filter best results per model if selected
+    view_best_per_model = st.radio(
+                                    "Select all results or best results",
+                                    ["best results per model", "all results"],
+                                    index=0,
+                                    key=unique_key,
+                                    horizontal=True
+                                )
+    if view_best_per_model == "best results per model":
+        table[iqm_column_name] = pd.to_numeric(table[iqm_column_name])
+        # table = table.loc[table.groupby('Model')[iqm_column_name].transform('idxmax'),:]
+        imax = table.groupby('Model')[iqm_column_name].idxmax(skipna=True)
+        imax =imax.to_frame().reset_index()
+        imax = [int(table[table["Model"] == row["Model"]].index[0]) if np.isnan(row[iqm_column_name]) else row[iqm_column_name] for i, row in imax.iterrows()]
+        table = table.loc[imax]
+        table = table.drop_duplicates(['Model'])
+
+    #filter by search bars
+    col1, col2  = st.columns(2)
+    with col1:
+        search_models_query = st.text_input(f"Search by model", "", key=f"search_{unique_key}_models")
+    with col2:
+        search_submission_query = st.text_input(f"Search by submission", "", key=f"search_{unique_key}_submission")
+    # with col3:
+    #     search_settings_query = st.text_input(f"Search by settings", "", key=f"search_{unique_key}_settings")
+    if search_models_query:
+        table = table[table['Model'].str.contains(search_models_query, case=False)]
+    if search_submission_query:
+        table = table[table['submission'].str.contains(search_submission_query, case=False)]
+    # if search_settings_query:
+    #     table = table[table['Config Settings'].str.contains(search_settings_query, case=False)]
+
+    # Sort values
+    table = table.sort_values(by=iqm_column_name, ascending=False)
+    table_err = table_err.loc[table.index]
+    table = table.drop(["index"], errors='ignore')
+    table_err = table_err.drop(["index"], errors='ignore')
+    return table, table_err
+
+
+
+def get_rank(df):
+    columns_to_rank = DATASETS + list(DIMENSIONS.keys())
+    df_rank = df.copy()
+    for col in df.columns:
+        if col in columns_to_rank:
+            df_rank[col] = df[col].rank(method='max', na_option='bottom', ascending=False)
+    return df_rank
+
+
+def create_overall_performance_tab(
+        overall_performance_tables, 
+        iqm_column_name='Core'
+        ):
+    # Main Leaderboard tab   
+    st.header("Capabilities Ranking")
+
+    #show full finetuning or frozen results if selected
+    view_frozen_or_full_ft = st.radio(
+                                    "Select full finetuning or frozen values",
+                                    ["fully finetuned backbone", "frozen backbone"],
+                                    index=0,
+                                    key="overall_ft_or_frozen",
+                                    horizontal=True
+                                )
+
+    # overall_performance_tables = overall_performance_tables["full_ft"].copy()
+    if view_frozen_or_full_ft == "fully finetuned backbone":
+        overall_performance_tables = overall_performance_tables["full_ft"].copy()
+    else:
+        overall_performance_tables = overall_performance_tables["frozen"].copy()
+    overall_table = overall_performance_tables["normalized"].copy()
+    overall_table_err = overall_performance_tables["normalized_err"].copy()
+
+    # filter with user selections
+    overall_table, overall_table_err =  filter_with_user_selections(unique_key="overall_all_or_best",
+                                                                    iqm_column_name = iqm_column_name,
+                                                                    table = overall_table, 
+                                                                    table_err = overall_table_err
+                                                                    )
+    if not overall_table.empty:
+        overall_table["submission"] = overall_table["submission"].str.split('-', expand = True)[0]
+        overall_table_err["submission"] = overall_table_err["submission"].str.split('-', expand = True)[0]
+
+
+    #convert to rank
+    rank_table = get_rank(overall_table)
+    
+    # Export the DataFrame to CSV
+    if st.button("Export to CSV", key=f"overall_performance_export_main"):
+        csv_data = overall_table.to_csv(index=False)
+        st.download_button(
+            label="Download CSV",
+            data=csv_data,
+            file_name=f"overall_performance_leaderboard.csv",
+            key="download-csv",
+            help="Click to download the CSV file",
+        )
+    st.markdown("Lower rank is better (1 = best). Colors are scaled per column. ")
+
+    html_table = create_html_results_table(overall_table, overall_table_err, rank_table, display_rank=True)
+    st.markdown(html_table, unsafe_allow_html=True)
+
+   
+
+def create_dimension_performance_tab(
+                        performance_by_dimension_tables
+                        ):
+    # Dimension  tab   
+    st.header("Performance By Capability")
+    
+    #show full finetuning or frozen results if selected
+    view_frozen_or_full_ft = st.radio(
+                                    "Select full finetuning or frozen values",
+                                    ["fully finetuned backbone", "frozen backbone"],
+                                    index=0,
+                                    key="dimension_ft_or_frozen",
+                                    horizontal=True
+                                )
+    if view_frozen_or_full_ft == "fully finetuned backbone":
+        performance_by_dimension_tables = performance_by_dimension_tables["full_ft"].copy()
+    else:
+        performance_by_dimension_tables = performance_by_dimension_tables["frozen"].copy()
+
+    #add drop down
+    dimension_drop_down = st.selectbox('Select dimension to view',
+                                        ([f"{key} - {value}" for key, value in DIMENSION_INFO.items()]))
+    dimension_drop_down = dimension_drop_down.split(" - ")[0]
+
+    dimension_table = performance_by_dimension_tables["normalized"][dimension_drop_down].copy()
+    dimension_table_err = performance_by_dimension_tables["normalized_err"][f"{dimension_drop_down}_err"].copy()
+    iqm_column_name = f'{dimension_drop_down}'
+
+    # filter with search bars
+    dimension_table, dimension_table_err  = filter_with_user_selections(unique_key = "dimension_all_or_best",
+                                                    iqm_column_name = iqm_column_name,
+                                                    table = dimension_table,
+                                                    table_err = dimension_table_err)
+    if not dimension_table.empty:
+        dimension_table["submission"] = dimension_table["submission"].str.split('-', expand = True)[0]
+        dimension_table_err["submission"] = dimension_table_err["submission"].str.split('-', expand = True)[0]
+
+    #convert to rank
+    rank_table = get_rank(dimension_table)
+    
+    #performance_by_dimension_tables[dimension_drop_down]['Model'] = performance_by_dimension_tables[dimension_drop_down]['Model'].apply(make_hyperlink)
+    html_table = create_html_results_table(dimension_table, dimension_table_err, rank_table, display_rank=False)
+    st.markdown(html_table, unsafe_allow_html=True)
+
+
+def create_datasets_tabs(all_datasets_tables: dict
+                            ):
+    datasets_tabs = st.tabs([dataset.replace("_", " ") for dataset in DATASETS]) 
+    for i, dataset in enumerate(DATASETS):
+        with datasets_tabs[i]:   
+            dataset_name = dataset.replace("_", " ").title()
+            dataset_desc = DATASET_INFO["Description"][DATASET_INFO["Dataset"].index(dataset_name)]
+            st.header(dataset.replace("_", " ").title())
+            st.markdown(dataset_desc)
+
+            #show full finetuning or frozen results if selected
+            view_frozen_or_full_ft = st.radio(
+                                            "Select full finetuning or frozen values",
+                                            ["fully finetuned backbone", "frozen backbone"],
+                                            index=0,
+                                            key=f"{dataset_name}_ft_or_frozen",
+                                            horizontal=True
+                                        )
+            if view_frozen_or_full_ft == "fully finetuned backbone":
+                datasets_tables = all_datasets_tables["full_ft"].copy()
+            else:
+                datasets_tables = all_datasets_tables["frozen"].copy()
+
+            #show raw or normalized results if selected
+            view_raw_or_normalized_dataset = st.radio(
+                                            "Select raw or normalized values",
+                                            ["normalized values (with IQM)", "raw values (with Mean)"],
+                                            index=0,
+                                            key=f"{dataset}_raw_or_normalized",
+                                            horizontal=True
+                                        )
+            dataset_table = datasets_tables["normalized"][dataset].copy()
+            dataset_table_err = datasets_tables["normalized_err"][dataset].copy()
+            # iqm_column_name = "IQM"
+            if view_raw_or_normalized_dataset == "normalized values (with IQM)":
+                dataset_table = datasets_tables["normalized"][dataset].copy()
+                dataset_table_err = datasets_tables["normalized_err"][dataset].copy()
+                iqm_column_name = "IQM"
+            else:
+                dataset_table = datasets_tables["raw"][dataset].copy()
+                dataset_table_err = datasets_tables["raw_err"][dataset].copy()
+                iqm_column_name = "Mean"
+            
+            # filter with search bars
+            dataset_table, dataset_table_err = filter_with_user_selections(unique_key = dataset,
+                                                        iqm_column_name = iqm_column_name,
+                                                        table = dataset_table,
+                                                        table_err = dataset_table_err
+                                                        )
+            if not dataset_table.empty:
+                dataset_table["submission"] = dataset_table["submission"].str.split('-', expand = True)[0]
+                dataset_table_err["submission"] = dataset_table_err["submission"].str.split('-', expand = True)[0]
+
+            #convert to rank
+            rank_table = get_rank(dataset_table)
+
+            #create html table
+            html_table = create_html_results_table(dataset_table, dataset_table_err, rank_table, display_rank=False)
+            st.markdown(html_table, unsafe_allow_html=True)
+
+
+
+def create_info_tab(submission_info_table):
+    # tabs = st.tabs(["Dataset Info", "Capability Info", "Submission Info"]) 
+    tabs = st.tabs([ "Capability Info"]) 
+
+    # with tabs[0]:
+    #     st.header("Dataset Info")
+        
+    #     dataset_table = pd.DataFrame(DATASET_INFO)
+    #     citation_hyperlinks = [make_hyperlink_datasets(url = row.Hyperlinks,
+    #                             url_name = row.Citation) for _, row in dataset_table.iterrows()]
+    #     dataset_table.drop(columns=['Hyperlinks', 'Citation'], inplace = True)
+    #     dataset_table["Citation"] = citation_hyperlinks
+    #     dataset_table = create_html_table_info(dataset_table)
+    #     st.markdown(dataset_table, unsafe_allow_html=True)
+
+    with tabs[0]:
+        st.header("Capability Info")
+        dims = []
+        datasets = []
+        details = []
+        for dimension, info in DIMENSION_INFO.items():
+            dims.append(dimension)
+            datasets.append(", ".join(DIMENSIONS[dimension]))
+            details.append(info)
+        dim_table = pd.DataFrame({
+                            "Capability": dims,
+                            "Details": details,
+                            "Datasets": datasets,
+                            })
+        dim_table = create_html_table_info(dim_table)
+        st.markdown(dim_table, unsafe_allow_html=True)
+
+    # with tabs[2]:
+    #     st.header("Submission Info")
+    #     dim_table = create_html_table_info(submission_info_table)
+    #     st.markdown(dim_table, unsafe_allow_html=True)
+
+    
+
+
+
+def main():
+    st.set_page_config(page_title="GeoBench Leaderboard", layout="wide", initial_sidebar_state="expanded")
+    st.markdown("""
+        <head>
+            <meta http-equiv="Content-Security-Policy" 
+                content="default-src 'self' https://huggingface.co;
+                        script-src 'self' 'unsafe-inline';
+                        style-src 'self' 'unsafe-inline';
+                        img-src 'self' data: https:;
+                        frame-ancestors 'none';">
+            <meta http-equiv="X-Frame-Options" content="DENY">
+            <meta http-equiv="X-Content-Type-Options" content="nosniff">
+            <meta http-equiv="Referrer-Policy" content="strict-origin-when-cross-origin">
+        </head>
+    """, unsafe_allow_html=True)
+
+    compiled_results = pd.read_pickle(f'{RESULTS_DIR}/compiled.pkl')
+    overall_performance_tables = compiled_results["overall_performance_tables"] 
+    performance_by_dimension_tables = compiled_results["performance_by_dimension_tables"] 
+    datasets_tables = compiled_results["datasets_tables"] 
+    submission_info_table = compiled_results["submission_info_table"] 
+    del compiled_results
+    
+    #create header
+    st.title("🏆 GEO-Bench Leaderboard")
+    st.markdown("Benchmarking Geospatial Foundation Models")
+    # content = create_yall()
+    tabs = st.tabs(["🏆 Main Leaderboard", "Capabilities", "Datasets", "Info", "📝 How to Submit"])
+
+    with tabs[0]:
+        create_overall_performance_tab(overall_performance_tables=overall_performance_tables)
+
+    with tabs[1]:
+        create_dimension_performance_tab(performance_by_dimension_tables=performance_by_dimension_tables)
+    
+    with tabs[2]:
+        # Datasets tabs               
+        #create individual dataset pages
+        create_datasets_tabs(all_datasets_tables=datasets_tables)
+
+    with tabs[3]:
+        # Info tab       
+        create_info_tab(submission_info_table)
+
+    with tabs[-1]:
+        #About page
+        st.header("How to Submit")
+        with open("utils/about_page.txt") as f:
+            about_page = f.read()
+        st.markdown(about_page)
+    comment = """ 
+
+    with tabs[2]:
+        # Models tab       
+        st.markdown("Models used for benchmarking")
+        model_tabs = st.tabs(all_model_names) 
+        #create individual benchmark pages
+        #create_models_tabs(all_submission_results=all_submission_results,
+        #                    model_tabs=model_tabs,
+        #                    all_model_names=all_model_names
+        #                    )
+    with tabs[3]:
+        # Submissions tab       
+        st.markdown("Experiments submitted to benchmark benchmarking")
+        submissions_tabs = st.tabs(all_submissions) 
+        #create individual benchmark pages
+        #create_submissions_tabs(all_submission_results=all_submission_results,
+        #                    model_tabs=submissions_tabs,
+        #                    all_submissions=all_submissions
+        #                    )
+     
+    """
+                
+        
+if __name__ == "__main__":
+    main()

examples/additional_info.json

@@ -0,0 +1,17 @@
+{
+
+    "Paper Link": "links to published paper or preprint if available. Please use N/A if not available",
+    "Code Repository Link ": "links to any papers that you wish to be included with your submission. Please use N/A if not available",
+    "License": "License information about your model, e.g. MIT. Please use N/A if not available",
+    "Number of HPO trials": "Number of trials for hyperparameter optimiztion of each model/dataset combination",
+    "Additional information about submission": "Any information about how the experiments were conducted",
+    "Comments on new models in submission": "Describe any new models you have included which were not previously part of the leaderboard. You may include information such as model architecture, pre-training datasets used, number of training steps, hardware used, training time",
+    "New model info": 
+        [
+            {
+                "model_display_name": "Name to be used in leaderboard table for any new models in your submission",
+                "model_size": "number of trainable parameters in model (e.g. 300M)", 
+                "unique_backbone_key": "backbone used in experiments to identify the model. Will be used to identify the model in the results table (csv file)"
+            }
+        ]
+}

examples/results_and_parameters.csv

@@ -0,0 +1,241 @@
+dataset,Metric,experiment_name,partition name,backbone,decoder,batch_size_selection,early_stop_patience,n_trials,Seed,data_percentages,batch_size,weight_decay,lr,test metric
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4074,100%,8,0.376311714631646,8.49502710213644e-05,0.6672176122665405
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1897,100%,8,0.376311714631646,8.49502710213644e-05,0.6660189628601074
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1270,100%,8,0.376311714631646,8.49502710213644e-05,0.6640214920043945
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3768,100%,8,0.376311714631646,8.49502710213644e-05,0.6960753202438354
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3401,100%,8,0.376311714631646,8.49502710213644e-05,0.6873977184295654
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3224,100%,8,0.376311714631646,8.49502710213644e-05,0.6798655986785889
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2874,100%,8,0.376311714631646,8.49502710213644e-05,0.651594340801239
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3985,100%,8,0.376311714631646,8.49502710213644e-05,0.6867294311523438
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4143,100%,8,0.376311714631646,8.49502710213644e-05,0.665279746055603
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,52,100%,8,0.376311714631646,8.49502710213644e-05,0.6851502656936646
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1448,100%,32,0.0028238171171985,0.0009324538851647,0.3234341442584991
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,584,100%,32,0.0028238171171985,0.0009324538851647,0.3291504979133606
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1031,100%,32,0.0028238171171985,0.0009324538851647,0.3228558301925659
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2751,100%,32,0.0028238171171985,0.0009324538851647,0.3260976672172546
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3898,100%,32,0.0028238171171985,0.0009324538851647,0.3153356313705444
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1970,100%,32,0.0028238171171985,0.0009324538851647,0.3248571753501892
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,96,100%,32,0.0028238171171985,0.0009324538851647,0.3324618935585022
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2531,100%,32,0.0028238171171985,0.0009324538851647,0.3320326507091522
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2268,100%,32,0.0028238171171985,0.0009324538851647,0.315716028213501
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3222,100%,32,0.0028238171171985,0.0009324538851647,0.3328837752342224
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,199,100%,8,0.0635019082538137,0.0001077432627767,0.933402419090271
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4121,100%,8,0.0635019082538137,0.0001077432627767,0.9397921562194824
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2249,100%,8,0.0635019082538137,0.0001077432627767,0.9408209323883056
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3809,100%,8,0.0635019082538137,0.0001077432627767,0.938472330570221
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,85,100%,8,0.0635019082538137,0.0001077432627767,0.939550518989563
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,780,100%,8,0.0635019082538137,0.0001077432627767,0.939792811870575
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1094,100%,8,0.0635019082538137,0.0001077432627767,0.9387277364730836
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2316,100%,8,0.0635019082538137,0.0001077432627767,0.938027560710907
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2244,100%,8,0.0635019082538137,0.0001077432627767,0.935537576675415
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1350,100%,8,0.0635019082538137,0.0001077432627767,0.9421088695526124
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1511,100%,8,0.1162518746995334,0.0009977774835077,0.9161623120307922
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2020,100%,8,0.1162518746995334,0.0009977774835077,0.7651582956314087
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2860,100%,8,0.1162518746995334,0.0009977774835077,0.7665423154830933
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3835,100%,8,0.1162518746995334,0.0009977774835077,0.9021945595741272
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1192,100%,8,0.1162518746995334,0.0009977774835077,0.9094558954238892
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2281,100%,8,0.1162518746995334,0.0009977774835077,0.890521228313446
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1968,100%,8,0.1162518746995334,0.0009977774835077,0.7655184268951416
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,218,100%,8,0.1162518746995334,0.0009977774835077,0.7658498883247375
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2115,100%,8,0.1162518746995334,0.0009977774835077,0.8599324226379395
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3423,100%,8,0.1162518746995334,0.0009977774835077,0.9091439843177797
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2509,100%,8,0.0983399202983907,0.0009153563094797,0.5734111666679382
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3987,100%,8,0.0983399202983907,0.0009153563094797,0.559952974319458
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3921,100%,8,0.0983399202983907,0.0009153563094797,0.5713885426521301
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,47,100%,8,0.0983399202983907,0.0009153563094797,0.5674113631248474
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,585,100%,8,0.0983399202983907,0.0009153563094797,0.5748375654220581
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2217,100%,8,0.0983399202983907,0.0009153563094797,0.570099949836731
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,288,100%,8,0.0983399202983907,0.0009153563094797,0.5726335048675537
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4135,100%,8,0.0983399202983907,0.0009153563094797,0.5661790370941162
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3220,100%,8,0.0983399202983907,0.0009153563094797,0.5695707201957703
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1504,100%,8,0.0983399202983907,0.0009153563094797,0.5611818432807922
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,515,100%,16,0.2992355570741097,0.0005669541997516,0.8232710361480713
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1577,100%,16,0.2992355570741097,0.0005669541997516,0.8244178295135498
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2509,100%,16,0.2992355570741097,0.0005669541997516,0.8250547647476196
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1281,100%,16,0.2992355570741097,0.0005669541997516,0.8269679546356201
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2456,100%,16,0.2992355570741097,0.0005669541997516,0.8278542160987854
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2575,100%,16,0.2992355570741097,0.0005669541997516,0.8266293406486511
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4495,100%,16,0.2992355570741097,0.0005669541997516,0.8163466453552246
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3347,100%,16,0.2992355570741097,0.0005669541997516,0.8090845346450806
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1611,100%,16,0.2992355570741097,0.0005669541997516,0.8216108083724976
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4208,100%,16,0.2992355570741097,0.0005669541997516,0.8205042481422424
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,799,100%,32,0.0845698013691995,7.095358602186485e-05,0.7135563492774963
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,339,100%,32,0.0845698013691995,7.095358602186485e-05,0.7104905843734741
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4739,100%,32,0.0845698013691995,7.095358602186485e-05,0.688521146774292
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4036,100%,32,0.0845698013691995,7.095358602186485e-05,0.7167086601257324
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3312,100%,32,0.0845698013691995,7.095358602186485e-05,0.715383768081665
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2740,100%,32,0.0845698013691995,7.095358602186485e-05,0.7025712728500366
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3843,100%,32,0.0845698013691995,7.095358602186485e-05,0.6929353475570679
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4613,100%,32,0.0845698013691995,7.095358602186485e-05,0.7135555744171143
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2118,100%,32,0.0845698013691995,7.095358602186485e-05,0.7066311836242676
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1998,100%,32,0.0845698013691995,7.095358602186485e-05,0.7069718241691589
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1273,100%,32,0.3169093481136426,0.0002803700277342,0.331969290971756
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4369,100%,32,0.3169093481136426,0.0002803700277342,0.3571399450302124
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4295,100%,32,0.3169093481136426,0.0002803700277342,0.3499228954315185
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3719,100%,32,0.3169093481136426,0.0002803700277342,0.3375613093376159
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4980,100%,32,0.3169093481136426,0.0002803700277342,0.3444221615791321
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3926,100%,32,0.3169093481136426,0.0002803700277342,0.3480183482170105
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,523,100%,32,0.3169093481136426,0.0002803700277342,0.3338068127632141
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3132,100%,32,0.3169093481136426,0.0002803700277342,0.3421605825424194
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,268,100%,32,0.3169093481136426,0.0002803700277342,0.3521450757980346
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1294,100%,32,0.3169093481136426,0.0002803700277342,0.3380949795246124
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2853,100%,32,0.2806807709516379,0.0001561962879473,0.9402685165405272
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,921,100%,32,0.2806807709516379,0.0001561962879473,0.935638189315796
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2885,100%,32,0.2806807709516379,0.0001561962879473,0.9274752140045166
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1833,100%,32,0.2806807709516379,0.0001561962879473,0.9409592151641846
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2434,100%,32,0.2806807709516379,0.0001561962879473,0.942594826221466
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4523,100%,32,0.2806807709516379,0.0001561962879473,0.9272408485412598
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1074,100%,32,0.2806807709516379,0.0001561962879473,0.9365390539169312
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2695,100%,32,0.2806807709516379,0.0001561962879473,0.9388547539711
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3339,100%,32,0.2806807709516379,0.0001561962879473,0.9340709447860718
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,778,100%,32,0.2806807709516379,0.0001561962879473,0.9414107799530028
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,391,100%,8,0.1872029616850694,0.0001583623070717,0.8811582922935486
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,125,100%,8,0.1872029616850694,0.0001583623070717,0.7500208616256714
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1471,100%,8,0.1872029616850694,0.0001583623070717,0.7552796006202698
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,185,100%,8,0.1872029616850694,0.0001583623070717,0.7548510432243347
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3220,100%,8,0.1872029616850694,0.0001583623070717,0.8831520676612854
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1477,100%,8,0.1872029616850694,0.0001583623070717,0.7554706335067749
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,27,100%,8,0.1872029616850694,0.0001583623070717,0.8891997337341309
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3893,100%,8,0.1872029616850694,0.0001583623070717,0.7583714723587036
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3109,100%,8,0.1872029616850694,0.0001583623070717,0.74748694896698
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1561,100%,8,0.1872029616850694,0.0001583623070717,0.7510426640510559
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,339,100%,8,0.0958805157666471,0.0006669639988907,0.567497730255127
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1119,100%,8,0.0958805157666471,0.0006669639988907,0.5694387555122375
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1598,100%,8,0.0958805157666471,0.0006669639988907,0.5681214928627014
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,59,100%,8,0.0958805157666471,0.0006669639988907,0.5635424852371216
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,299,100%,8,0.0958805157666471,0.0006669639988907,0.5690293312072754
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3969,100%,8,0.0958805157666471,0.0006669639988907,0.5734402537345886
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1335,100%,8,0.0958805157666471,0.0006669639988907,0.5750075578689575
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1205,100%,8,0.0958805157666471,0.0006669639988907,0.5757657289505005
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1984,100%,8,0.0958805157666471,0.0006669639988907,0.5712120532989502
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1387,100%,8,0.0958805157666471,0.0006669639988907,0.5796757936477661
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2986,100%,32,0.2406274751665054,0.0007374796618441,0.8232717514038086
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3692,100%,32,0.2406274751665054,0.0007374796618441,0.8008925914764404
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2829,100%,32,0.2406274751665054,0.0007374796618441,0.8172112107276917
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3583,100%,32,0.2406274751665054,0.0007374796618441,0.8212674856185913
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4245,100%,32,0.2406274751665054,0.0007374796618441,0.8171231746673584
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4544,100%,32,0.2406274751665054,0.0007374796618441,0.8219597339630127
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1204,100%,32,0.2406274751665054,0.0007374796618441,0.8255108594894409
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,15,100%,32,0.2406274751665054,0.0007374796618441,0.8165488243103027
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4111,100%,32,0.2406274751665054,0.0007374796618441,0.8116637468338013
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2099,100%,32,0.2406274751665054,0.0007374796618441,0.7985596060752869
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4565,100%,32,0.0483818466583478,0.0009007718997571,0.9809809923171996
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4322,100%,32,0.0483818466583478,0.0009007718997571,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4148,100%,32,0.0483818466583478,0.0009007718997571,0.9819819927215576
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,16,100%,32,0.0483818466583478,0.0009007718997571,0.977977991104126
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3799,100%,32,0.0483818466583478,0.0009007718997571,0.9679679870605468
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4370,100%,32,0.0483818466583478,0.0009007718997571,0.9719719886779784
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4217,100%,32,0.0483818466583478,0.0009007718997571,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2748,100%,32,0.0483818466583478,0.0009007718997571,0.978978991508484
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4964,100%,32,0.0483818466583478,0.0009007718997571,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,145,100%,32,0.0483818466583478,0.0009007718997571,0.9589589834213256
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1582,100%,16,0.1420142212351907,6.484318042499172e-05,0.517241358757019
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3140,100%,16,0.1420142212351907,6.484318042499172e-05,0.568965494632721
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1849,100%,16,0.1420142212351907,6.484318042499172e-05,0.5557809472084045
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1966,100%,16,0.1420142212351907,6.484318042499172e-05,0.5030425786972046
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3231,100%,16,0.1420142212351907,6.484318042499172e-05,0.5425963401794434
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2190,100%,16,0.1420142212351907,6.484318042499172e-05,0.5486815571784973
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4762,100%,16,0.1420142212351907,6.484318042499172e-05,0.5679513216018677
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3672,100%,16,0.1420142212351907,6.484318042499172e-05,0.5415821671485901
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1329,100%,16,0.1420142212351907,6.484318042499172e-05,0.5446247458457947
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1399,100%,16,0.1420142212351907,6.484318042499172e-05,0.5334685444831848
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3310,100%,32,0.0016925260317968,6.455143990010064e-05,0.9869869947433472
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,535,100%,32,0.0016925260317968,6.455143990010064e-05,0.9819819927215576
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,562,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1753,100%,32,0.0016925260317968,6.455143990010064e-05,0.9849849939346312
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2622,100%,32,0.0016925260317968,6.455143990010064e-05,0.9649649858474731
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,814,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,237,100%,32,0.0016925260317968,6.455143990010064e-05,0.9839839935302734
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1307,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4435,100%,32,0.0016925260317968,6.455143990010064e-05,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4089,100%,32,0.0016925260317968,6.455143990010064e-05,0.9839839935302734
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4838,100%,32,0.2103480107442391,6.471079283583433e-05,0.6599027514457703
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,713,100%,32,0.2103480107442391,6.471079283583433e-05,0.6716534495353699
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4468,100%,32,0.2103480107442391,6.471079283583433e-05,0.6648851037025452
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,840,100%,32,0.2103480107442391,6.471079283583433e-05,0.6675868034362793
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1144,100%,32,0.2103480107442391,6.471079283583433e-05,0.6821687817573547
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1829,100%,32,0.2103480107442391,6.471079283583433e-05,0.6789093017578125
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2007,100%,32,0.2103480107442391,6.471079283583433e-05,0.6702918410301208
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2254,100%,32,0.2103480107442391,6.471079283583433e-05,0.6647154688835144
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,205,100%,32,0.2103480107442391,6.471079283583433e-05,0.6651250123977661
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,913,100%,32,0.2103480107442391,6.471079283583433e-05,0.6596580743789673
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3130,100%,32,0.290578170946364,8.388751435377213e-05,0.9739999771118164
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1220,100%,32,0.290578170946364,8.388751435377213e-05,0.9629999995231628
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1188,100%,32,0.290578170946364,8.388751435377213e-05,0.962000012397766
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2923,100%,32,0.290578170946364,8.388751435377213e-05,0.9700000286102296
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2033,100%,32,0.290578170946364,8.388751435377213e-05,0.968999981880188
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,445,100%,32,0.290578170946364,8.388751435377213e-05,0.972000002861023
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2729,100%,32,0.290578170946364,8.388751435377213e-05,0.9679999947547911
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3499,100%,32,0.290578170946364,8.388751435377213e-05,0.962000012397766
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1144,100%,32,0.290578170946364,8.388751435377213e-05,0.9679999947547911
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2902,100%,32,0.290578170946364,8.388751435377213e-05,0.9739999771118164
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,707,100%,16,0.3941167807260976,0.0002822095144255,0.4521651566028595
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3382,100%,16,0.3941167807260976,0.0002822095144255,0.4692849814891815
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4829,100%,16,0.3941167807260976,0.0002822095144255,0.448136955499649
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4496,100%,16,0.3941167807260976,0.0002822095144255,0.4360523521900177
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4976,100%,16,0.3941167807260976,0.0002822095144255,0.4189325273036957
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1421,100%,16,0.3941167807260976,0.0002822095144255,0.4269889295101166
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3807,100%,16,0.3941167807260976,0.0002822095144255,0.4390735030174255
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3103,100%,16,0.3941167807260976,0.0002822095144255,0.4491440057754516
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1716,100%,16,0.3941167807260976,0.0002822095144255,0.4471299052238464
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3421,100%,16,0.3941167807260976,0.0002822095144255,0.4290030300617218
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1006,100%,16,0.0233859235441379,0.0008743862145496,0.9689689874649048
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,855,100%,16,0.0233859235441379,0.0008743862145496,0.9759759902954102
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,274,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3967,100%,16,0.0233859235441379,0.0008743862145496,0.966966986656189
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1261,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3907,100%,16,0.0233859235441379,0.0008743862145496,0.977977991104126
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,507,100%,16,0.0233859235441379,0.0008743862145496,0.9699699878692628
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4890,100%,16,0.0233859235441379,0.0008743862145496,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,821,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,897,100%,16,0.0233859235441379,0.0008743862145496,0.9759759902954102
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2709,100%,8,0.2143980952203471,7.678537962381587e-05,0.546653151512146
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4385,100%,8,0.2143980952203471,7.678537962381587e-05,0.523326575756073
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2334,100%,8,0.2143980952203471,7.678537962381587e-05,0.5537525415420532
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2296,100%,8,0.2143980952203471,7.678537962381587e-05,0.563894510269165
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2335,100%,8,0.2143980952203471,7.678537962381587e-05,0.540567934513092
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4220,100%,8,0.2143980952203471,7.678537962381587e-05,0.5567951202392578
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,600,100%,8,0.2143980952203471,7.678537962381587e-05,0.5415821671485901
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4085,100%,8,0.2143980952203471,7.678537962381587e-05,0.5081135630607605
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3605,100%,8,0.2143980952203471,7.678537962381587e-05,0.4898580014705658
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4851,100%,8,0.2143980952203471,7.678537962381587e-05,0.5578093528747559
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2466,100%,32,0.0241049339778712,0.0006317522129654,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2541,100%,32,0.0241049339778712,0.0006317522129654,0.9829829931259156
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2246,100%,32,0.0241049339778712,0.0006317522129654,0.978978991508484
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,132,100%,32,0.0241049339778712,0.0006317522129654,0.9689689874649048
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1691,100%,32,0.0241049339778712,0.0006317522129654,0.9729729890823364
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1696,100%,32,0.0241049339778712,0.0006317522129654,0.9799799919128418
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2224,100%,32,0.0241049339778712,0.0006317522129654,0.9799799919128418
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4222,100%,32,0.0241049339778712,0.0006317522129654,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1077,100%,32,0.0241049339778712,0.0006317522129654,0.9739739894866944
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3614,100%,32,0.0241049339778712,0.0006317522129654,0.95695698261261
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4838,100%,32,0.0006609967274643,0.0001244735178777,0.6715825200080872
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,713,100%,32,0.0006609967274643,0.0001244735178777,0.6624463200569153
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4468,100%,32,0.0006609967274643,0.0001244735178777,0.6741133332252502
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,840,100%,32,0.0006609967274643,0.0001244735178777,0.6756007671356201
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1144,100%,32,0.0006609967274643,0.0001244735178777,0.6727983355522156
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1829,100%,32,0.0006609967274643,0.0001244735178777,0.6618573069572449
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2007,100%,32,0.0006609967274643,0.0001244735178777,0.6743751168251038
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2254,100%,32,0.0006609967274643,0.0001244735178777,0.6706086993217468
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,205,100%,32,0.0006609967274643,0.0001244735178777,0.6765933632850647
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,913,100%,32,0.0006609967274643,0.0001244735178777,0.6776209473609924
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1698,100%,16,0.1849003044425631,0.0001063634870237,0.9539999961853028
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3146,100%,16,0.1849003044425631,0.0001063634870237,0.9539999961853028
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4144,100%,16,0.1849003044425631,0.0001063634870237,0.9639999866485596
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3064,100%,16,0.1849003044425631,0.0001063634870237,0.9660000205039978
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2803,100%,16,0.1849003044425631,0.0001063634870237,0.9430000185966492
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1624,100%,16,0.1849003044425631,0.0001063634870237,0.9760000109672546
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,614,100%,16,0.1849003044425631,0.0001063634870237,0.9559999704360962
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2735,100%,16,0.1849003044425631,0.0001063634870237,0.9580000042915344
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2931,100%,16,0.1849003044425631,0.0001063634870237,0.9639999866485596
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2808,100%,16,0.1849003044425631,0.0001063634870237,0.9480000138282776
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,272,100%,32,0.0028788214595454,6.027942222485105e-05,0.4803625345230102
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,719,100%,32,0.0028788214595454,6.027942222485105e-05,0.5075528621673584
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2115,100%,32,0.0028788214595454,6.027942222485105e-05,0.5629405975341797
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4503,100%,32,0.0028788214595454,6.027942222485105e-05,0.5156092643737793
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,940,100%,32,0.0028788214595454,6.027942222485105e-05,0.5276938676834106
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3361,100%,32,0.0028788214595454,6.027942222485105e-05,0.5236656665802002
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,543,100%,32,0.0028788214595454,6.027942222485105e-05,0.5085599422454834
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1667,100%,32,0.0028788214595454,6.027942222485105e-05,0.5357502698898315
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1312,100%,32,0.0028788214595454,6.027942222485105e-05,0.52064448595047
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3415,100%,32,0.0028788214595454,6.027942222485105e-05,0.5287008881568909

normalization_example.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2352e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from utils.compute_tools import make_normalizer, load_normalizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a3a5a5e",
+   "metadata": {},
+   "source": [
+    "## 1. normalization with max-min from your own results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e95b280a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read results from csv file\n",
+    "results = pd.read_csv(f\"examples/results_and_parameters.csv\")\n",
+    "\n",
+    "benchmark_name=\"your_benchmark_name\" #set your desired benchmark_name\n",
+    "metric = \"test metric\" #column containing test metrics in csv file\n",
+    "make_normalizer(\n",
+    "    results, \n",
+    "    metrics=(metric,), \n",
+    "    benchmark_name=benchmark_name\n",
+    "    )\n",
+    "\n",
+    "#normalize results\n",
+    "normalizer = load_normalizer(benchmark_name=benchmark_name)\n",
+    "new_metric = normalizer.normalize_data_frame(df=results, metric=metric)\n",
+    "\n",
+    "#save normalized values to file\n",
+    "results.to_csv(\"examples/normalized_results_and_parameters.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d3f1e10",
+   "metadata": {},
+   "source": [
+    "## 2. normalization with max-min from leaderboard base results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07a21745",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read results from csv file\n",
+    "results = pd.read_csv(f\"examples/results_and_parameters.csv\")\n",
+    "\n",
+    "metric = \"test metric\" #column containing test metrics in csv file\n",
+    "make_normalizer(\n",
+    "    results, \n",
+    "    metrics=(metric,), \n",
+    "    # benchmark_name=benchmark_name\n",
+    "    )\n",
+    "\n",
+    "#normalize results\n",
+    "normalizer = load_normalizer() #leave benchmark name blank to use default leaderboard normalization values\n",
+    "new_metric = normalizer.normalize_data_frame(df=results, metric=metric)\n",
+    "\n",
+    "#save normalized values to file\n",
+    "results.to_csv(\"examples/normalized_results_and_parameters.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -0,0 +1,9 @@
+# streamlit==#1.23
+streamlit==1.50.0
+pandas==2.2.3
+# requests
+# plotly
+# gistyc
+huggingface_hub==0.34.4
+scipy==1.13.1
+numpy==2.0.2

results/114be1f0-5a41-43a5-b4e6-7fb683bc01ec/additional_info.json

@@ -0,0 +1 @@
+{"Paper Link": "", "Code Repository Link ": "https://github.com/The-AI-Alliance/GEO-Bench-2", "License": "Apache 2.0", "Number of HPO trials": "10",  "Number of repeated seeds trials": "5", "Additional information about submission": "These experiments were conducted as part of the release of the GEO-Bench-V2 datasets.", "Comments on new models in submission": "Describe any new models you have included which were not previously part of the leaderboard", "New model info": [{"model_display_name": "", "model_size": "", "unique_backbone_key": ""}]}

results/2ce4a907-7ae3-45d3-a07a-558f8d0d758b/additional_info.json

@@ -0,0 +1 @@
+{"Paper Link": "", "Code Repository Link ": "https://github.com/The-AI-Alliance/GEO-Bench-2", "License": "Apache 2.0", "Number of HPO trials": "10", "Additional information about submission": "These experiments were conducted as part of the release of the GEO-Bench-V2 datasets. The results differ from the main experimenst in that they use SAR input bands (vv and vh) for backbones with those bands. Additionally, the best performance is selected between between multi-modal (Sentinel1 + Sentinel2) results and Sentinel2 only results.", "Comments on new models in submission": "Describe any new models you have included which were not previously part of the leaderboard", "New model info": [{"model_display_name": "", "model_size": "", "unique_backbone_key": ""}]}

results/2ce4a907-7ae3-45d3-a07a-558f8d0d758b/results_and_parameters.csv

@@ -0,0 +1,286 @@
+dataset,Metric,test metric,mlflow_run_name,mlflow_run_id,mlflow_run_status,Seed,experiment_id,experiment_name,index,batch_size,lr,decoder,backbone,early_stop_patience,n_trials,partition_name,data_percentages,batch_size_selection,weight_decay,partition name,frozen_or_full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.7027350068092346,kuro_siwo_mean_1829,8bf0b18e42534cfeb28c95c4d223b139,FINISHED,1829,352948250742384064,final_main_dofa_large_patch16_224,0,16,2.1734651841007975e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.696682870388031,kuro_siwo_mean_2007,8cf11995a0a041bf964f50e6a59208e3,FINISHED,2007,352948250742384064,final_main_dofa_large_patch16_224,0,16,2.1734651841007975e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6902852058410645,kuro_siwo_mean_913,b478d9f5d81f421081d91948db7e9118,FINISHED,913,352948250742384064,final_main_dofa_large_patch16_224,0,16,2.1734651841007975e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6930838823318481,kuro_siwo_mean_2254,f9d5eaef63694c96aefa64d6c0717971,FINISHED,2254,352948250742384064,final_main_dofa_large_patch16_224,0,16,2.1734651841007975e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.7017667293548584,kuro_siwo_mean_205,fb918750211d4607b77be91ac4386db3,FINISHED,205,352948250742384064,final_main_dofa_large_patch16_224,0,16,2.1734651841007975e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7661927342414856,benv2_913,93bfa5abd6624479a57f50dd7b652d89,FINISHED,913,187261838745675157,final_main_terramind_v1_large,0,32,1.6854640185950023e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7627988457679749,benv2_2007,9ed47b3c62734b0885809c27f83ef0ea,FINISHED,2007,187261838745675157,final_main_terramind_v1_large,0,32,1.6854640185950023e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7654047012329102,benv2_205,a72a8e86fbfe4f0aad0f04c844343b64,FINISHED,205,187261838745675157,final_main_terramind_v1_large,0,32,1.6854640185950023e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7620870471000671,benv2_2254,bef1608c103b440c8127ecfabc902510,FINISHED,2254,187261838745675157,final_main_terramind_v1_large,0,32,1.6854640185950023e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7664718627929688,benv2_1829,ee32af4990e64a7cb4eeaf0ee1878703,FINISHED,1829,187261838745675157,final_main_terramind_v1_large,0,32,1.6854640185950023e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9600617068058296,biomassters_3709,0d6679e9363847a293d595f7036727a1,FINISHED,3709,187261838745675157,final_main_terramind_v1_large,0,16,3.5687963643724935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.960075930313406,biomassters_2004,7c38d0013d9c4b739103ae70c72bae42,FINISHED,2004,187261838745675157,final_main_terramind_v1_large,0,16,3.5687963643724935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9600184425339132,biomassters_517,835f47675ee64b78a247c37581f0bbc6,FINISHED,517,187261838745675157,final_main_terramind_v1_large,0,16,3.5687963643724935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9599535499078812,biomassters_3212,d44f8450d0b644018b2fb7f3777a251c,FINISHED,3212,187261838745675157,final_main_terramind_v1_large,0,16,3.5687963643724935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9600944613389646,biomassters_2181,e357564f8040497c8b79637f5be1aaf3,FINISHED,2181,187261838745675157,final_main_terramind_v1_large,0,16,3.5687963643724935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6434163451194763,treesatai_1829,2c9433a138bd4e54a59a843bb6097fb6,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,16,1.4856303554806574e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6427352428436279,treesatai_913,5f8c37a366114e73afca33786775ec18,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,16,1.4856303554806574e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6456222534179688,treesatai_2254,7efd840abb6f439fb5fc51295ce4ad14,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,16,1.4856303554806574e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6432837843894958,treesatai_205,80c571e4fa52436e9485732821a35d02,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,16,1.4856303554806574e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6295856237411499,treesatai_2007,914141bf92794bbd8e7635c0a12caecc,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,16,1.4856303554806574e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6083433628082275,cloudsen12_2007,0c7bfe38cdd9408b9a7c4ea368549d05,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.01219601133369e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6143325567245483,cloudsen12_913,193a0cb48eb442f4828c1b6bc26d6094,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.01219601133369e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6288191080093384,cloudsen12_1829,6aa2aab0569d488cbbdd650fc3f6ee03,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.01219601133369e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6256763339042664,cloudsen12_205,abbbced195a345c484c4f81a6b9a3a41,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.01219601133369e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.5966204404830933,cloudsen12_2254,b392c01b01dc4cde8ee5871bf71bfe8f,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.01219601133369e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2227849066257476,dynamic_earthnet_2007,274e7995347f47fa842330476948b6f8,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,8,0.0001225410111414,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.1974807381629943,dynamic_earthnet_2254,5e45f39ec2dc45f5b37350653b93fdab,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,8,0.0001225410111414,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2498252987861633,dynamic_earthnet_1829,88d23a9ef15240c6a56d52f7884187bf,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,8,0.0001225410111414,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2542161047458648,dynamic_earthnet_205,bf867a28362c4b17a7bb54c8db19895a,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,8,0.0001225410111414,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3165871500968933,dynamic_earthnet_913,f09a0b7ead7d40918d715f7286cf2001,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,8,0.0001225410111414,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5438066720962524,forestnet_2007,4d6bcb60f3d641a89bc6c1de9b92ff27,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.529707471348659e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5548841953277588,forestnet_205,4f531a7701dc4b2cbd52d6a45e5421f9,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.529707471348659e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5518630146980286,forestnet_1829,9785e60235e947e384dec4fc3d7d6770,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.529707471348659e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5417925715446472,forestnet_913,b223f3941d494872980ff3befd0c8b93,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.529707471348659e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5709969997406006,forestnet_2254,cc43ff06c90e4a00aedf53dde37fe8b4,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,16,7.529707471348659e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8707944750785828,spacenet2_2254,752b0a638ce9427f9b3fbad0b3d3892b,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.5596487626570115e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8679686188697815,spacenet2_205,9b77ff78cb0b45c085ce433239bf391d,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.5596487626570115e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.868889570236206,spacenet2_2007,c8191395b80f455099bb26301dd0f888,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.5596487626570115e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8709847927093506,spacenet2_1829,f3cf167614db49f1887353607ff0adab,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.5596487626570115e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8698230981826782,spacenet2_913,fe23665c5bfb47ad8a8082ef00ec9e34,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.5596487626570115e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5704560279846191,fotw_2254,34410287f59b41e39c0bd8bba66129f7,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,32,8.128380314143639e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5707604885101318,fotw_1829,5f876fe2f93f4c798d4ae1a8cab0e438,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,32,8.128380314143639e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5685666799545288,fotw_205,622e20a7dcf844f8a90073f8495aa4ec,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,32,8.128380314143639e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5764850378036499,fotw_913,62bdc75f599a42688f836dddda074c3f,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,32,8.128380314143639e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5762674808502197,fotw_2007,8fb093eed1594b93888c9321ea6f5d99,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,32,8.128380314143639e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6207649111747742,caffe_2007,43c6708d451c4ca4bcfec193f889d855,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,8,8.979909406203737e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6483673453330994,caffe_205,45b92a6aca2a45b69a2a914d1e8dfe65,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,8,8.979909406203737e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6169370412826538,caffe_2254,71384199fef54e389d97a5aa9df05c24,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,8,8.979909406203737e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6701884269714355,caffe_913,94f135f67e9b4115a1e21d9368900c4d,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,8,8.979909406203737e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6350060105323792,caffe_1829,cea6958e29654faeb171394d1b426f7c,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,8,8.979909406203737e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8865088224411011,burn_scars_1829,47ef0e9e50574d4193f047b61fa419af,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.860940083914165e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8995709419250488,burn_scars_2007,4cdd7cb0a402479cbd309863d0d1709f,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.860940083914165e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8901950120925903,burn_scars_913,87d867e03ed74dd9aa50a64920eca51b,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.860940083914165e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8866046667098999,burn_scars_205,8ace465629784870918db6887da14778,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.860940083914165e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8964208960533142,burn_scars_2254,d2ef7358586449f3b5c8745c80cb42d2,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,32,1.860940083914165e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.3643054366111755,pastis_3828,175c080fbf164875b9eb0755b26d1a85,FINISHED,3828,602968560747083429,final_main_dofa_large_patch16_224,0,16,0.0001772335383556,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.3606434166431427,pastis_4498,843693ed0acf4446b609c367e8486bab,FINISHED,4498,602968560747083429,final_main_dofa_large_patch16_224,0,16,0.0001772335383556,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.373330682516098,pastis_3994,a8f6992888884b3fbb798642ed9d4223,FINISHED,3994,602968560747083429,final_main_dofa_large_patch16_224,0,16,0.0001772335383556,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.3611724972724914,pastis_4959,d56fe442d7494230ae309402f4e980ed,FINISHED,4959,602968560747083429,final_main_dofa_large_patch16_224,0,16,0.0001772335383556,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.3584975600242615,pastis_2138,e7050f80f2a043628e3ac34fe600f90f,FINISHED,2138,602968560747083429,final_main_dofa_large_patch16_224,0,16,0.0001772335383556,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7438075542449951,benv2_2007,0b48b11457214766ba7f73274f3ab6ae,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,8,1.8432765430149102e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.746505856513977,benv2_1829,5386f90e29804fa285c408ad8caf0aca,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,8,1.8432765430149102e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7387846112251282,benv2_2254,59bdca639654431597ca6ee7e1b7e0a7,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,8,1.8432765430149102e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7331004738807678,benv2_205,b17277691f384aa19b68b6027f6585a3,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,8,1.8432765430149102e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7418051958084106,benv2_913,d7b9751ad6a54accaf2eaf9b92bb8837,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,8,1.8432765430149102e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5852291584014893,spacenet7_1829,20621f87303540459bfdd532a776a22d,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,16,6.896963289323795e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5895407795906067,spacenet7_2007,4eecaa23d018446aacf10a46ba9fc68f,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,16,6.896963289323795e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5664384365081787,spacenet7_913,933a40f9d02f481bbdf40327e922d24a,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,16,6.896963289323795e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5767611861228943,spacenet7_205,a971911204de4355bcd98dcd0e24ef42,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,16,6.896963289323795e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5797898769378662,spacenet7_2254,f4ced3e0f6ae4199b4e8cea45c7a154a,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,16,6.896963289323795e-05,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5243004560470581,flair2_1829,175928a05cc34d54bf95b6582d9af28a,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,8,6.8882846462248425e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5314410328865051,flair2_913,57dae9985db04f4595cf3975759a86a5,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,8,6.8882846462248425e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5262851715087891,flair2_2254,66efe7141914457abc40f48d003a2eef,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,8,6.8882846462248425e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5236417651176453,flair2_205,6bf6974e97614498bf62b94dd59b3297,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,8,6.8882846462248425e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5276963710784912,flair2_2007,d57653ff21014f0891e061cfdf6740cc,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,8,6.8882846462248425e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6237322688102722,so2sat_205,1c9fbeded1774ffa8967e5f05bace4cb,FINISHED,205,602968560747083429,final_main_dofa_large_patch16_224,0,32,7.704775944514587e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6277890205383301,so2sat_2007,233e7095159142dc839ac2274e9fa407,FINISHED,2007,602968560747083429,final_main_dofa_large_patch16_224,0,32,7.704775944514587e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6135902404785156,so2sat_2254,5923296a78184290b94ea7b2f27bd6e5,FINISHED,2254,602968560747083429,final_main_dofa_large_patch16_224,0,32,7.704775944514587e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6085192561149597,so2sat_913,7c3a1ffccf14403cbe16645f37f7a52e,FINISHED,913,602968560747083429,final_main_dofa_large_patch16_224,0,32,7.704775944514587e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6004056930541992,so2sat_1829,918f557298d54fe7a8b51b4998c006d2,FINISHED,1829,602968560747083429,final_main_dofa_large_patch16_224,0,32,7.704775944514587e-06,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9558932320251284,biomassters_205,18a3169412e648159ba6a0329eda4892,FINISHED,205,744656354097600381,final_main_dofa_large_patch16_224,0,32,0.0001600422501584,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9561515885579914,biomassters_913,264415783a664a0780da1e88a91fce93,FINISHED,913,744656354097600381,final_main_dofa_large_patch16_224,0,32,0.0001600422501584,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9563273420989667,biomassters_2254,8214cbc3006148499bf9b4d7a04135b1,FINISHED,2254,744656354097600381,final_main_dofa_large_patch16_224,0,32,0.0001600422501584,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9562463463899084,biomassters_2007,e0049a87b64044f4b0f78a00dfff7864,FINISHED,2007,744656354097600381,final_main_dofa_large_patch16_224,0,32,0.0001600422501584,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.955642991325092,biomassters_1829,e811b895b9524bcf91623cfd9d1d2782,FINISHED,1829,744656354097600381,final_main_dofa_large_patch16_224,0,32,0.0001600422501584,UNet,dofa_large_patch16_224,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9573879029464148,biomassters_2007,1036c854e21d4f1c8a733ce8732aef4c,FINISHED,2007,255611966290211098,final_main_clay_v1_base,0,8,0.0001510693802786,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9575604343580482,biomassters_205,3619b9b9de7a4d4ba80605b72c0eaf9a,FINISHED,205,255611966290211098,final_main_clay_v1_base,0,8,0.0001510693802786,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.9575122120913346,biomassters_2254,4d7f37d3b0ea4eaa8b842bf92c2332a6,FINISHED,2254,255611966290211098,final_main_clay_v1_base,0,8,0.0001510693802786,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.957540802816482,biomassters_913,749f5759aec34b6bb5d54845357e3a50,FINISHED,913,255611966290211098,final_main_clay_v1_base,0,8,0.0001510693802786,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+biomassters,RMSE,0.957558218198665,biomassters_1829,e705392713f9476d84d260d9cca94eac,FINISHED,1829,255611966290211098,final_main_clay_v1_base,0,8,0.0001510693802786,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6082392334938049,treesatai_2007,033840637b5b413e98c8c4a93c354a16,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,1.884527322686325e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6026829481124878,treesatai_1829,07316201f3c74ad686d396651fd15b4c,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,1.884527322686325e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6134301424026489,treesatai_913,88a1381a6edc457c8c68f0e79a0f8c56,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,1.884527322686325e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6063681840896606,treesatai_205,9ce93db3dbf747a2946ffa5636242e0c,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,1.884527322686325e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6038383841514587,treesatai_2254,fef3efd9ec8c4e51ab53f57a5679d62d,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,1.884527322686325e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6427658796310425,cloudsen12_913,6a46441f69f64b198c9aa59e4d6f307c,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,7.996310139856582e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6333767175674438,cloudsen12_2007,7782abff14e94db2b0a475a2630cab11,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,7.996310139856582e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6486442685127258,cloudsen12_2254,784dea19a46849f4bb145c4073c2501e,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,7.996310139856582e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6247012615203857,cloudsen12_205,c7b9d65df3724ed6b821d90ad35ef43e,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,7.996310139856582e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6452000141143799,cloudsen12_1829,e705b56b8ca74d578d2fac8d7ac06e02,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,7.996310139856582e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3228030800819397,dynamic_earthnet_1829,03bc8f8838ea4802a90819bd781eda99,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,7.302073764938037e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3438019752502441,dynamic_earthnet_2254,12d3ed6f16e340e4a70fbf3cc66c1bff,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,7.302073764938037e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3542783260345459,dynamic_earthnet_913,5d1e4ce6a5304f65a91d2bb9dcba64ee,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,7.302073764938037e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3484990894794464,dynamic_earthnet_2007,78971c3f274d4a579bbfcc32ce4f2df7,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,7.302073764938037e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3561779260635376,dynamic_earthnet_205,bdcae37855d74a6a852f9702bc76ca7b,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,7.302073764938037e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5468277931213379,forestnet_2254,3d88a92d7de64496947a75d68028456b,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,4.156509715420542e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5317220687866211,forestnet_2007,578df2d3a74e4fc4bdcba6cee3ca7c10,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,4.156509715420542e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5236656665802002,forestnet_913,837ee66991224ef4aec5db5cbc0a22f3,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,4.156509715420542e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5367572903633118,forestnet_205,8aec0a7f54ec4514b99be1f6d74156b6,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,4.156509715420542e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5266867876052856,forestnet_1829,d9fd4a9c061743fda49691f78f6c616a,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,4.156509715420542e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8624110221862793,spacenet2_1829,2589d12ccad34d0fafff972296a68a80,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,32,3.8520774982759184e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8709872364997864,spacenet2_2254,4607fe38394749d9a16bd205d2943311,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,32,3.8520774982759184e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8712811470031738,spacenet2_2007,64653fa24c6f4a8da05bbb261e57b5c0,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,32,3.8520774982759184e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8687437176704407,spacenet2_205,b6aabd890d37409c994e0b6e0f1f8eb4,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,32,3.8520774982759184e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8694702386856079,spacenet2_913,c9c64e3a5efd44a39802d01cb1ed49b6,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,32,3.8520774982759184e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5768706798553467,fotw_1829,1b4b4cb204954795821566a0b8f6c5c0,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,16,4.67119403305935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5796996355056763,fotw_913,2789ce0d3f414345b0ab718a4feff4af,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,16,4.67119403305935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5840237140655518,fotw_2254,65b8b4dbd0f640338073f3d55c146505,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,16,4.67119403305935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.5741722583770752,fotw_205,96e331aa9efa41998981739178b04578,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,16,4.67119403305935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.579285740852356,fotw_2007,aae42db4d03c4e3898a08f25bca7b816,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,16,4.67119403305935e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6605269908905029,caffe_913,34e240db26b04acb8d01c291db903a94,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,16,8.278042946046306e-06,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6455444097518921,caffe_205,4bda602a46ce4b37815e2f5576bce4db,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,16,8.278042946046306e-06,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6482639908790588,caffe_2254,6ea7f2a51c004c249defb15d2286fb74,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,16,8.278042946046306e-06,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6465308666229248,caffe_2007,cf5fe701018e4f03a4c97cfebf3fce6d,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,16,8.278042946046306e-06,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6363961696624756,caffe_1829,fd610248f29549528d27829254790f07,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,16,8.278042946046306e-06,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8888662457466125,burn_scars_1829,069c2eea2c92431d96a3bed9ca1db52c,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,32,7.44952527451419e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8771705627441406,burn_scars_913,604edeaf9ac8486585043f88341db731,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,32,7.44952527451419e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8922334909439087,burn_scars_2007,ae63d7f5a18c408e8a79534739ca8a27,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,32,7.44952527451419e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.879090428352356,burn_scars_205,cde4507650344235928f720a894d81c2,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,32,7.44952527451419e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.881941020488739,burn_scars_2254,de59f1e05c74467faa757aef6f54bea7,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,32,7.44952527451419e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4315944612026214,pastis_1829,0d2e2c0ae76c437e8f499d3045082f93,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,8.464490029955513e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.43324875831604,pastis_913,35c99424df74475884e1cc988421f252,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,8.464490029955513e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4276216924190521,pastis_205,5fc80028bb4e4e1fb4e583f9fba648a4,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,8.464490029955513e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4324474334716797,pastis_2007,6f6dc06bd68346d9a0c631d073a49d5b,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,8.464490029955513e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4276187121868133,pastis_2254,c9c88eaf8a0b4aa691efc66fcd743ce1,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,8.464490029955513e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5814478993415833,spacenet7_913,410b02e0a346456abd8dfbf35219f687,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,8,0.0001048690356486,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5786465406417847,spacenet7_1829,4a0ece393cce4b568d751ebb1035747d,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,8,0.0001048690356486,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5747677683830261,spacenet7_205,872cc3ef97324871ba9d246575a81713,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,8,0.0001048690356486,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5899662375450134,spacenet7_2254,955c7be0b4084cacb9901afec6eb5755,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,8,0.0001048690356486,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.5882823467254639,spacenet7_2007,e235ab18157447f7b5ed47c9e7018136,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,8,0.0001048690356486,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.4562640488147735,flair2_2007,0bbb60bbc0cb48eb8afe360ec4e36f05,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,32,2.156939442656292e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.4799627661705017,flair2_1829,35636fa259814f38839d09d0e6121ea1,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,32,2.156939442656292e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.4654695093631744,flair2_205,8a6daf00a44c40f8bad810d2752b9435,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,32,2.156939442656292e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.475152850151062,flair2_913,a319297b35f844aa88d6fc6b743e0ffe,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,32,2.156939442656292e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.4621029496192932,flair2_2254,b54a1010d9534657ade333bd719db283,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,32,2.156939442656292e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6176470518112183,so2sat_1829,4aff70ce17d749218b387abd72222e2e,FINISHED,1829,450828154493670864,final_main_terramind_v1_large,0,16,1.1833931015226612e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6237322688102722,so2sat_2254,9abcf955cd1442a5a03c9fe9fefde591,FINISHED,2254,450828154493670864,final_main_terramind_v1_large,0,16,1.1833931015226612e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6176470518112183,so2sat_2007,c66f9fde32e84bc6843965abde8c5c9e,FINISHED,2007,450828154493670864,final_main_terramind_v1_large,0,16,1.1833931015226612e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.6541582345962524,so2sat_913,cccbd9b5a7b74830836c5fec3c2509a5,FINISHED,913,450828154493670864,final_main_terramind_v1_large,0,16,1.1833931015226612e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.5882353186607361,so2sat_205,cf558c8ef6c741349661bc5fedbe40b7,FINISHED,205,450828154493670864,final_main_terramind_v1_large,0,16,1.1833931015226612e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6343602538108826,treesatai_1829,12ea388a636f4203a47d4785008610aa,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,8,5.344035791344604e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6290724277496338,treesatai_913,2b86d48dcfd640ca825673845b00059c,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,8,5.344035791344604e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6141548752784729,treesatai_2007,9b96c12e36624079bcce5bd97a6f1167,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,8,5.344035791344604e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6324618458747864,treesatai_2254,9ca6b9d928ac4a38b3946670739e9451,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,8,5.344035791344604e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+treesatai,Multilabel_F1_Score,0.6322035789489746,treesatai_205,cce64706c75041df86536f44a90e215c,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,8,5.344035791344604e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6351529359817505,cloudsen12_913,48774f9a97624315ae3e886eacfe0435,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,16,0.0001394626650077,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6364178657531738,cloudsen12_205,91aae5ab96c248a4a722e307d3ff1b03,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,16,0.0001394626650077,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6383556127548218,cloudsen12_1829,abb4ff93602c4003bc642c230a0ffda8,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,16,0.0001394626650077,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6329723000526428,cloudsen12_2007,ca5fac56485143c9ac4ec944472cea76,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,16,0.0001394626650077,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+cloudsen12,Multiclass_Jaccard_Index,0.6371685266494751,cloudsen12_2254,f74250c15b2c43d38785625fbd4bf222,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,16,0.0001394626650077,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.3079797029495239,dynamic_earthnet_205,6a138209c8694d358d3f034656644388,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,8,0.0002096829865225,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2587114870548248,dynamic_earthnet_2007,7fabb3c69acb4642a0d1d1e6f57e85d7,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,8,0.0002096829865225,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2470388710498809,dynamic_earthnet_2254,8dac79da836a4574abdd0e26a24f1701,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,8,0.0002096829865225,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.2762714624404907,dynamic_earthnet_1829,952228b5f26a4f6b90f9dde90980738c,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,8,0.0002096829865225,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+dynamic_earthnet,Multiclass_Jaccard_Index,0.246768832206726,dynamic_earthnet_913,9694beac28d24665be7ad33feca9ead5,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,8,0.0002096829865225,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5488418936729431,forestnet_205,37ea0560f7d34e918062ec3f831e65cb,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,32,0.0001223388869719,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5317220687866211,forestnet_2254,4f1d76022532497f934a9678d9fbebc6,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,32,0.0001223388869719,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5005035400390625,forestnet_913,8b315bccf68c4147882564e29ac4a1ac,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,32,0.0001223388869719,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5518630146980286,forestnet_2007,9a473930c9454ba6b38a046346cb5fca,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,32,0.0001223388869719,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+forestnet,Overall_Accuracy,0.5065458416938782,forestnet_1829,dae6cc8e0d0b410d9c3f496f5b13ed0e,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,32,0.0001223388869719,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8664951324462891,spacenet2_913,13ebb9b45c1a4ba2a54d73e569d27fe2,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,8,6.071043686824564e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8692799806594849,spacenet2_1829,176909c954a74b418a0de6bee5925a01,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,8,6.071043686824564e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8709346055984497,spacenet2_2007,1a51dba9c52d47c19c48f7139c4df1d1,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,8,6.071043686824564e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.870833694934845,spacenet2_205,4ff74e0221944c95856d066cadec3a23,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,8,6.071043686824564e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet2,Multiclass_Jaccard_Index,0.8743444681167603,spacenet2_2254,51ba890b2c0a4f2cb2cda126adfa5b5c,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,8,6.071043686824564e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.6070407629013062,fotw_2007,1ba9a8e35df942ffa6f91a6f598bd5bf,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,8,5.520798769772461e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.6036347150802612,fotw_205,36e2bc873a784e30aeebfea7f24927b2,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,8,5.520798769772461e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.605728268623352,fotw_2254,67db7eeadc1048f7af0410bad101314c,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,8,5.520798769772461e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.6179081797599792,fotw_913,b7f204a7be1d4a07b2008020f8ad6c04,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,8,5.520798769772461e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+fotw,Multiclass_Jaccard_Index,0.6066063642501831,fotw_1829,edbdae2b1e2b4ec99b1bf31559200d41,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,8,5.520798769772461e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6686878204345703,caffe_2007,0f542956e329458f8f01111b3cf83449,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,32,4.593751430162973e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6210277080535889,caffe_205,46f663557ee24e209fc8a210da6682b9,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,32,4.593751430162973e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6501009464263916,caffe_1829,5659720de54545d3b6a525e52d83811d,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,32,4.593751430162973e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6309058666229248,caffe_2254,a4d26ba70ce4410e8e428c3e77e46eaf,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,32,4.593751430162973e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+caffe,Multiclass_Jaccard_Index,0.6710712909698486,caffe_913,e35ded1248c94b3480dcd08cab302d2b,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,32,4.593751430162973e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.9009078741073608,burn_scars_4364,580a0ba49acf4476a14ebd6a4066e2b9,FINISHED,4364,795717026824507105,final_main_clay_v1_base,0,32,3.749218153450784e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8782541155815125,burn_scars_656,98323884f1de46f987cc3a68e542c2ef,FINISHED,656,795717026824507105,final_main_clay_v1_base,0,32,3.749218153450784e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.9017086625099182,burn_scars_2070,b0d45ad78cfd496e9cd7c93acfa3b1b5,FINISHED,2070,795717026824507105,final_main_clay_v1_base,0,32,3.749218153450784e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8936687707901001,burn_scars_948,e5c80d7cd324434a9cdfdc426ac916a2,FINISHED,948,795717026824507105,final_main_clay_v1_base,0,32,3.749218153450784e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+burn_scars,Multiclass_Jaccard_Index,0.8911995887756348,burn_scars_2647,f9fd13f44f124fe595ced9ab301a29e7,FINISHED,2647,795717026824507105,final_main_clay_v1_base,0,32,3.749218153450784e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4113161563873291,pastis_1829,0a46d9a17f5148ad93e1e0c1e81976b5,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,8,0.0002878510754975,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4224403500556946,pastis_2254,255628a1ac8049fa870af21d49782a0d,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,8,0.0002878510754975,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4202279448509216,pastis_2007,279e2ee9edde4e36a16966a6073c2a32,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,8,0.0002878510754975,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4170020818710327,pastis_913,32b47f39049d4cdaba5750b9e64bb8ef,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,8,0.0002878510754975,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+pastis,Multiclass_Jaccard_Index,0.4030401408672333,pastis_205,73ff8f6483f24f65821ecb01126c1bd0,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,8,0.0002878510754975,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7354453206062317,benv2_2007,086018e6cf9b488d85c0e7d81139ce51,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,32,3.33308410048262e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7400927543640137,benv2_1829,57c035db70c74d0ab020c3d2a9aa5b9d,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,32,3.33308410048262e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7335430383682251,benv2_913,6addd7ee828b4be0a7584f7bdca2afa5,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,32,3.33308410048262e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7419769763946533,benv2_205,8ba51de7b6dd4590a48dbdad5c99c184,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,32,3.33308410048262e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+benv2,Multilabel_F1_Score,0.7317450642585754,benv2_2254,e91a6a1bb8a048ffaa799ab1b4cb26e4,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,32,3.33308410048262e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.6278140544891357,spacenet7_913,23931df3647a447b98924afd9156e50e,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,16,9.950013781622509e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.6355001330375671,spacenet7_2007,595b9164f38e422b80b23b61c34c21bb,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,16,9.950013781622509e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.6344202756881714,spacenet7_1829,d6682647027e4c60aa546b7403296c6b,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,16,9.950013781622509e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.6402554512023926,spacenet7_205,e20cf0add6fa4906acacf152506635fd,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,16,9.950013781622509e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+spacenet7,Multiclass_Jaccard_Index,0.6243715882301331,spacenet7_2254,f858df0a0ba94f1cae42ed00d8d5b33c,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,16,9.950013781622509e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.492638885974884,flair2_205,5476a3801d5f449ba585fc6473c8f53d,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,16,1.4518986919464469e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.4999808669090271,flair2_1829,5b1c518f22404da39871f98576c1376a,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,16,1.4518986919464469e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5131868124008179,flair2_2254,95e366099be140bd95156f8d71ff2c10,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,16,1.4518986919464469e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5013031363487244,flair2_2007,a72e987af7bb4aa38892b0e0e24aff39,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,16,1.4518986919464469e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+flair2,Multiclass_Jaccard_Index,0.5008986592292786,flair2_913,c2b858dd4ffc48d49d9ec3a356bfe233,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,16,1.4518986919464469e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.5507099628448486,so2sat_2254,10d5e2f859bb49e9a6252b112f3ef092,FINISHED,2254,795717026824507105,final_main_clay_v1_base,0,32,1.4417679593699067e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.5578093528747559,so2sat_913,1c0232ea31b34ad3bba5983612afbfeb,FINISHED,913,795717026824507105,final_main_clay_v1_base,0,32,1.4417679593699067e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.568965494632721,so2sat_2007,5029b5bf18434ee9958318436284e1f2,FINISHED,2007,795717026824507105,final_main_clay_v1_base,0,32,1.4417679593699067e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.5730223059654236,so2sat_1829,5733b4cd3632496fb824ada576e8ad98,FINISHED,1829,795717026824507105,final_main_clay_v1_base,0,32,1.4417679593699067e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+so2sat,Overall_Accuracy,0.5618661046028137,so2sat_205,f7d42ca5ada84507bb353e3644a74879,FINISHED,205,795717026824507105,final_main_clay_v1_base,0,32,1.4417679593699067e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6840789318084717,kuro_siwo_mean_1829,4c2be4598c644fd0b61c85b2d17ce145,FINISHED,1829,611310520608025673,final_main_clay_v1_base,0,32,1.069117674720793e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6429914236068726,kuro_siwo_mean_2254,4c6c31d9375441c3b086bdf090c8f48a,FINISHED,2254,611310520608025673,final_main_clay_v1_base,0,32,1.069117674720793e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6720964312553406,kuro_siwo_mean_2007,7bd0283d097a4358a55c28b26c2b0c50,FINISHED,2007,611310520608025673,final_main_clay_v1_base,0,32,1.069117674720793e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6733152866363525,kuro_siwo_mean_913,95587d8274b34a208e705d3388dd5c36,FINISHED,913,611310520608025673,final_main_clay_v1_base,0,32,1.069117674720793e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6832083463668823,kuro_siwo_mean_205,a681bef139c14037998046d381ec95a7,FINISHED,205,611310520608025673,final_main_clay_v1_base,0,32,1.069117674720793e-05,UNet,clay_v1_base,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6789531707763672,kuro_siwo_mean_2254,24a013f2b77e410580d24fe52254ca4e,FINISHED,2254,312067339846697973,final_main_terramind_v1_large,0,16,1.3485828940767237e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6908279657363892,kuro_siwo_mean_1829,43772c94fa25443eac7673c7333bc702,FINISHED,1829,312067339846697973,final_main_terramind_v1_large,0,16,1.3485828940767237e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6873027086257935,kuro_siwo_mean_2007,951baf4f4998489ba0b6069a3602f4b1,FINISHED,2007,312067339846697973,final_main_terramind_v1_large,0,16,1.3485828940767237e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6866209506988525,kuro_siwo_mean_205,a506606b93294a1685e36fe339d18780,FINISHED,205,312067339846697973,final_main_terramind_v1_large,0,16,1.3485828940767237e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+kuro_siwo,Multiclass_Jaccard_Index,0.6846413612365723,kuro_siwo_mean_913,ea3e86de5bce4b4ba6090546e9878756,FINISHED,913,312067339846697973,final_main_terramind_v1_large,0,16,1.3485828940767237e-05,UNet,terramind_v1_large,10.0,16,default,100,HPO,0.01,100%,full_ft
+everwatch,test_test_map,0.2534341514110565,everwatch_3669,607ff8a988e045e68ae3e8e5145cc65e,FINISHED,3669,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.691840880402376e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2444937378168106,everwatch_1163,c4346300038844f3833248da7c14956c,FINISHED,1163,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.691840880402376e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2528373301029205,everwatch_2007,2e68a653c4b74fdbba13de6799990a3c,FINISHED,2007,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.691840880402376e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2408047318458557,everwatch_2254,71fd1a06ab684647bad3ca7142349d1f,FINISHED,2254,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.691840880402376e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.245848074555397,everwatch_205,70dbe01088394f579c43f3d80e1357a1,FINISHED,205,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.691840880402376e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3451495170593261,nzcattle_4020,aa483311d2714806b5cc2551f7a71388,FINISHED,4020,514820428842584591,final_object_detection_terramind_v1_large,0,8,2.150367200036458e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3386348187923431,nzcattle_1180,d2d3ae07261f4d76ae875af0d00bf7fe,FINISHED,1180,514820428842584591,final_object_detection_terramind_v1_large,0,8,2.150367200036458e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3409347534179687,nzcattle_3354,ccd565bb21f441a89e464e6e858fec64,FINISHED,3354,514820428842584591,final_object_detection_terramind_v1_large,0,8,2.150367200036458e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3414294719696045,nzcattle_4871,442bc1da5b744dc6ad3e9086b0a84884,FINISHED,4871,514820428842584591,final_object_detection_terramind_v1_large,0,8,2.150367200036458e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3391881287097931,nzcattle_1790,2d51ebefd6574536ae33c608f78d30c6,FINISHED,1790,514820428842584591,final_object_detection_terramind_v1_large,0,8,2.150367200036458e-05,faster-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1280479282140731,pastis_4313,32d0c674a3d6441b9cf0c6fea0cab306,FINISHED,4313,514820428842584591,final_object_detection_terramind_v1_large,0,8,5.2730296596573885e-05,mask-rcnn,terramind_v1_large,,4,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1275206357240677,pastis_2125,67a62400fdba4d16ae11d7d6c40eda2f,FINISHED,2125,514820428842584591,final_object_detection_terramind_v1_large,0,8,5.2730296596573885e-05,mask-rcnn,terramind_v1_large,,4,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1302018165588379,pastis_3576,fcff661e40d5465e9ea336968d594257,FINISHED,3576,514820428842584591,final_object_detection_terramind_v1_large,0,8,5.2730296596573885e-05,mask-rcnn,terramind_v1_large,,4,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1362016499042511,pastis_1673,7f19707882684f55b36798dfb5f67023,FINISHED,1673,514820428842584591,final_object_detection_terramind_v1_large,0,8,5.2730296596573885e-05,mask-rcnn,terramind_v1_large,,4,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.126174196600914,pastis_1678,2c172aa30a9a4e9a8e09d80596e32eb8,FINISHED,1678,514820428842584591,final_object_detection_terramind_v1_large,0,8,5.2730296596573885e-05,mask-rcnn,terramind_v1_large,,4,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1580913513898849,substation_4764,f6fb3a1bc126438ab82ffec06d89c024,FINISHED,4764,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.394085192664972e-05,mask-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1446995437145233,substation_1138,6e0e3224732341779ca67e34aeda5b0a,FINISHED,1138,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.394085192664972e-05,mask-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1584698110818863,substation_1805,7f4f18d42461450693ea592ea630c97f,FINISHED,1805,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.394085192664972e-05,mask-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1500434279441833,substation_4921,3f49d728a57c482e8bb9f59c25b5b10c,FINISHED,4921,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.394085192664972e-05,mask-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1528779715299606,substation_1801,93ceb5f36f2644b0a4094fcf3bddbec6,FINISHED,1801,514820428842584591,final_object_detection_terramind_v1_large,0,8,3.394085192664972e-05,mask-rcnn,terramind_v1_large,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2631446421146393,everwatch_1829,3e9944e718034dcaa18fe5d32822bbdc,FINISHED,1829,605357125122862239,final_object_detection_clay_v1_base,0,8,7.652701145321595e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2578109502792358,everwatch_2007,dd5562d2693849f3b603df50366981ea,FINISHED,2007,605357125122862239,final_object_detection_clay_v1_base,0,8,7.652701145321595e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2597305476665497,everwatch_2254,6523f9dac5864b999dce1077947bafc4,FINISHED,2254,605357125122862239,final_object_detection_clay_v1_base,0,8,7.652701145321595e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2562801539897918,everwatch_205,ba802c3c038b428f8c07c4486347a50e,FINISHED,205,605357125122862239,final_object_detection_clay_v1_base,0,8,7.652701145321595e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.263173758983612,everwatch_913,5a55f1a6fc9340c890341e77583d976b,FINISHED,913,605357125122862239,final_object_detection_clay_v1_base,0,8,7.652701145321595e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3433267176151275,nzcattle_2878,cc9b58c831554391830ef770464d21a1,FINISHED,2878,605357125122862239,final_object_detection_clay_v1_base,0,8,3.675848053411779e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3414545059204101,nzcattle_2088,c80129ed5b1540688ff3a0de0d0e423d,FINISHED,2088,605357125122862239,final_object_detection_clay_v1_base,0,8,3.675848053411779e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3471623063087463,nzcattle_1420,17ca9f9a108d4fba8d2c9dd7774e4c35,FINISHED,1420,605357125122862239,final_object_detection_clay_v1_base,0,8,3.675848053411779e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3390698134899139,nzcattle_1316,10862f77a83f4272bdeddcdcde84fa64,FINISHED,1316,605357125122862239,final_object_detection_clay_v1_base,0,8,3.675848053411779e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3334003686904907,nzcattle_2898,f78d430daf7d4e198f3a40872d762bf4,FINISHED,2898,605357125122862239,final_object_detection_clay_v1_base,0,8,3.675848053411779e-05,faster-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1573373824357986,pastis_4287,6989719382474e31b7cd41827593983e,FINISHED,4287,605357125122862239,final_object_detection_clay_v1_base,0,8,0.000295799043128,mask-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1556365340948104,pastis_2044,acf176fbb38d41f5836dffd09221f78e,FINISHED,2044,605357125122862239,final_object_detection_clay_v1_base,0,8,0.000295799043128,mask-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1638926863670349,pastis_3626,e64ed324953245d9814fda1785f070cf,FINISHED,3626,605357125122862239,final_object_detection_clay_v1_base,0,8,0.000295799043128,mask-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1620885282754898,pastis_2880,128c077ccba54d06b53bd53d08bc16f3,FINISHED,2880,605357125122862239,final_object_detection_clay_v1_base,0,8,0.000295799043128,mask-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1555455029010772,pastis_1271,c81a2e84028c4bddae00e00cf60bf829,FINISHED,1271,605357125122862239,final_object_detection_clay_v1_base,0,8,0.000295799043128,mask-rcnn,clay_v1_base,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.198972001671791,substation_2899,2c2276e170c045ae90d8c0440f68fcfe,FINISHED,2899,605357125122862239,final_object_detection_clay_v1_base,0,8,4.9702442981039857e-05,mask-rcnn,clay_v1_base,,6,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1973665207624435,substation_1837,f930c9445ebf466c889344896f9ad3d4,FINISHED,1837,605357125122862239,final_object_detection_clay_v1_base,0,8,4.9702442981039857e-05,mask-rcnn,clay_v1_base,,6,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.19160096347332,substation_2583,30832073123e4f18bd051cac5fbc513a,FINISHED,2583,605357125122862239,final_object_detection_clay_v1_base,0,8,4.9702442981039857e-05,mask-rcnn,clay_v1_base,,6,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1949329674243927,substation_4485,de389f4138c04a37a770a63c036f2094,FINISHED,4485,605357125122862239,final_object_detection_clay_v1_base,0,8,4.9702442981039857e-05,mask-rcnn,clay_v1_base,,6,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1865809261798858,substation_3778,361b1d2a4fc243c1868a4f7cf7761c29,FINISHED,3778,605357125122862239,final_object_detection_clay_v1_base,0,8,4.9702442981039857e-05,mask-rcnn,clay_v1_base,,6,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2216494381427765,everwatch_664,8d1e47f96e634f42979e464052cdfbce,FINISHED,664,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,1.5772858997826887e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2342153042554855,everwatch_2007,8df959ba52f44fa69865555170eaa59a,FINISHED,2007,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,1.5772858997826887e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2346295565366745,everwatch_2254,25052567b1b5439bb8b327a090967a4c,FINISHED,2254,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,1.5772858997826887e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2428609728813171,everwatch_205,d2c28bfc9f59487480ed8a4bdf0a2e73,FINISHED,205,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,1.5772858997826887e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+everwatch,test_test_map,0.2463185787200927,everwatch_913,f8a4dbc6da5b49deac6c79fc5252c6d0,FINISHED,913,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,1.5772858997826887e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3440934121608734,nzcattle_3375,3c78817882634a348e0b976417312b13,FINISHED,3375,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,3.682421948556654e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3322223424911499,nzcattle_1120,5243544454c4457687bdf7ca5340837a,FINISHED,1120,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,3.682421948556654e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3285744786262512,nzcattle_4742,c080c1aa72a04602b1d821fe53b26fe2,FINISHED,4742,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,3.682421948556654e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.3344000875949859,nzcattle_3847,68ee59823ac9428db537b49b575bc489,FINISHED,3847,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,3.682421948556654e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+nzcattle,test_test_map,0.337315559387207,nzcattle_4215,4e5fa5a126044620b07a59096d620b5d,FINISHED,4215,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,3.682421948556654e-05,faster-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1026935577392578,pastis_885,88c0de7d9ee848f29a28c4bde131b71e,FINISHED,885,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,0.0001740641909195,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.0998783111572265,pastis_4188,67e76b5416974309846e9708fcf355bf,FINISHED,4188,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,0.0001740641909195,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1050681769847869,pastis_4067,b25cadfd6b7a48f886c48914d0a45f22,FINISHED,4067,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,0.0001740641909195,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1043741926550865,pastis_2609,06f19abc035d40da883c8b4087771b22,FINISHED,2609,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,0.0001740641909195,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+pastis_r,test_test_segm_map,0.1034035980701446,pastis_2098,2467fdaf645a45a98df442d2d6d1ccfe,FINISHED,2098,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,0.0001740641909195,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1499142944812774,substation_3512,ec22279f62444ba1848325643a2d34ff,FINISHED,3512,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,2.7770838121582783e-05,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1436789482831955,substation_2416,c4e30467f8da4cef859d32a5da79492c,FINISHED,2416,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,2.7770838121582783e-05,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1456765234470367,substation_407,f05798af79c74a119dafda7600051afc,FINISHED,407,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,2.7770838121582783e-05,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1477409899234771,substation_3559,4155d7a698ea4b8daa23b6b6de0c21a1,FINISHED,3559,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,2.7770838121582783e-05,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft
+substation,test_test_segm_map,0.1337875574827194,substation_3022,8511a4e01ee145cca3f19167b550276c,FINISHED,3022,735470102071628624,final_object_detection_dofa_large_patch16_224,0,8,2.7770838121582783e-05,mask-rcnn,dofa_large_patch16_224,,10,default,100,Fixed,0.01,100%,full_ft

results/80100fc6-dc1a-4514-b946-341157eaf816/additional_info.json

@@ -0,0 +1 @@
+{"Paper Link": "", "Code Repository Link ": "https://github.com/The-AI-Alliance/GEO-Bench-2", "License": "Apache 2.0", "Number of HPO trials": "10",  "Number of repeated seeds trials": "5", "Additional information about submission": "These experiments were conducted as part of the release of the GEO-Bench-V2 datasets.", "Comments on new models in submission": "Describe any new models you have included which were not previously part of the leaderboard", "New model info": [{"model_display_name": "", "model_size": "", "unique_backbone_key": ""}]}

results/compiled.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:664778491bc8bf64e436b9303d8221406666a620a40966dfc75f64e3f8af0f8d
+size 131166

tests/resources/inputs/test_submission_1/results_and_parameters.csv

@@ -0,0 +1,241 @@
+dataset,Metric,experiment_name,partition name,backbone,decoder,batch_size_selection,early_stop_patience,n_trials,Seed,data_percentages,batch_size,weight_decay,lr,test metric
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4074,100%,8,0.376311714631646,8.49502710213644e-05,0.6672176122665405
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1897,100%,8,0.376311714631646,8.49502710213644e-05,0.6660189628601074
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1270,100%,8,0.376311714631646,8.49502710213644e-05,0.6640214920043945
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3768,100%,8,0.376311714631646,8.49502710213644e-05,0.6960753202438354
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3401,100%,8,0.376311714631646,8.49502710213644e-05,0.6873977184295654
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3224,100%,8,0.376311714631646,8.49502710213644e-05,0.6798655986785889
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2874,100%,8,0.376311714631646,8.49502710213644e-05,0.651594340801239
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3985,100%,8,0.376311714631646,8.49502710213644e-05,0.6867294311523438
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4143,100%,8,0.376311714631646,8.49502710213644e-05,0.665279746055603
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,52,100%,8,0.376311714631646,8.49502710213644e-05,0.6851502656936646
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1448,100%,32,0.0028238171171985,0.0009324538851647,0.3234341442584991
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,584,100%,32,0.0028238171171985,0.0009324538851647,0.3291504979133606
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1031,100%,32,0.0028238171171985,0.0009324538851647,0.3228558301925659
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2751,100%,32,0.0028238171171985,0.0009324538851647,0.3260976672172546
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3898,100%,32,0.0028238171171985,0.0009324538851647,0.3153356313705444
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1970,100%,32,0.0028238171171985,0.0009324538851647,0.3248571753501892
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,96,100%,32,0.0028238171171985,0.0009324538851647,0.3324618935585022
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2531,100%,32,0.0028238171171985,0.0009324538851647,0.3320326507091522
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2268,100%,32,0.0028238171171985,0.0009324538851647,0.315716028213501
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3222,100%,32,0.0028238171171985,0.0009324538851647,0.3328837752342224
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,199,100%,8,0.0635019082538137,0.0001077432627767,0.933402419090271
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4121,100%,8,0.0635019082538137,0.0001077432627767,0.9397921562194824
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2249,100%,8,0.0635019082538137,0.0001077432627767,0.9408209323883056
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3809,100%,8,0.0635019082538137,0.0001077432627767,0.938472330570221
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,85,100%,8,0.0635019082538137,0.0001077432627767,0.939550518989563
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,780,100%,8,0.0635019082538137,0.0001077432627767,0.939792811870575
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1094,100%,8,0.0635019082538137,0.0001077432627767,0.9387277364730836
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2316,100%,8,0.0635019082538137,0.0001077432627767,0.938027560710907
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2244,100%,8,0.0635019082538137,0.0001077432627767,0.935537576675415
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1350,100%,8,0.0635019082538137,0.0001077432627767,0.9421088695526124
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1511,100%,8,0.1162518746995334,0.0009977774835077,0.9161623120307922
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2020,100%,8,0.1162518746995334,0.0009977774835077,0.7651582956314087
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2860,100%,8,0.1162518746995334,0.0009977774835077,0.7665423154830933
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3835,100%,8,0.1162518746995334,0.0009977774835077,0.9021945595741272
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1192,100%,8,0.1162518746995334,0.0009977774835077,0.9094558954238892
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2281,100%,8,0.1162518746995334,0.0009977774835077,0.890521228313446
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1968,100%,8,0.1162518746995334,0.0009977774835077,0.7655184268951416
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,218,100%,8,0.1162518746995334,0.0009977774835077,0.7658498883247375
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2115,100%,8,0.1162518746995334,0.0009977774835077,0.8599324226379395
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3423,100%,8,0.1162518746995334,0.0009977774835077,0.9091439843177797
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2509,100%,8,0.0983399202983907,0.0009153563094797,0.5734111666679382
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3987,100%,8,0.0983399202983907,0.0009153563094797,0.559952974319458
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3921,100%,8,0.0983399202983907,0.0009153563094797,0.5713885426521301
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,47,100%,8,0.0983399202983907,0.0009153563094797,0.5674113631248474
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,585,100%,8,0.0983399202983907,0.0009153563094797,0.5748375654220581
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2217,100%,8,0.0983399202983907,0.0009153563094797,0.570099949836731
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,288,100%,8,0.0983399202983907,0.0009153563094797,0.5726335048675537
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4135,100%,8,0.0983399202983907,0.0009153563094797,0.5661790370941162
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3220,100%,8,0.0983399202983907,0.0009153563094797,0.5695707201957703
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1504,100%,8,0.0983399202983907,0.0009153563094797,0.5611818432807922
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,515,100%,16,0.2992355570741097,0.0005669541997516,0.8232710361480713
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1577,100%,16,0.2992355570741097,0.0005669541997516,0.8244178295135498
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2509,100%,16,0.2992355570741097,0.0005669541997516,0.8250547647476196
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1281,100%,16,0.2992355570741097,0.0005669541997516,0.8269679546356201
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2456,100%,16,0.2992355570741097,0.0005669541997516,0.8278542160987854
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,2575,100%,16,0.2992355570741097,0.0005669541997516,0.8266293406486511
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4495,100%,16,0.2992355570741097,0.0005669541997516,0.8163466453552246
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,3347,100%,16,0.2992355570741097,0.0005669541997516,0.8090845346450806
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,1611,100%,16,0.2992355570741097,0.0005669541997516,0.8216108083724976
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,smp_Unet,optimized,50,16,4208,100%,16,0.2992355570741097,0.0005669541997516,0.8205042481422424
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,799,100%,32,0.0845698013691995,7.095358602186485e-05,0.7135563492774963
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,339,100%,32,0.0845698013691995,7.095358602186485e-05,0.7104905843734741
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4739,100%,32,0.0845698013691995,7.095358602186485e-05,0.688521146774292
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4036,100%,32,0.0845698013691995,7.095358602186485e-05,0.7167086601257324
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3312,100%,32,0.0845698013691995,7.095358602186485e-05,0.715383768081665
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2740,100%,32,0.0845698013691995,7.095358602186485e-05,0.7025712728500366
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3843,100%,32,0.0845698013691995,7.095358602186485e-05,0.6929353475570679
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4613,100%,32,0.0845698013691995,7.095358602186485e-05,0.7135555744171143
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2118,100%,32,0.0845698013691995,7.095358602186485e-05,0.7066311836242676
+chesapeake,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1998,100%,32,0.0845698013691995,7.095358602186485e-05,0.7069718241691589
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1273,100%,32,0.3169093481136426,0.0002803700277342,0.331969290971756
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4369,100%,32,0.3169093481136426,0.0002803700277342,0.3571399450302124
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4295,100%,32,0.3169093481136426,0.0002803700277342,0.3499228954315185
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3719,100%,32,0.3169093481136426,0.0002803700277342,0.3375613093376159
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4980,100%,32,0.3169093481136426,0.0002803700277342,0.3444221615791321
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3926,100%,32,0.3169093481136426,0.0002803700277342,0.3480183482170105
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,523,100%,32,0.3169093481136426,0.0002803700277342,0.3338068127632141
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3132,100%,32,0.3169093481136426,0.0002803700277342,0.3421605825424194
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,268,100%,32,0.3169093481136426,0.0002803700277342,0.3521450757980346
+sa_crop_type,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1294,100%,32,0.3169093481136426,0.0002803700277342,0.3380949795246124
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2853,100%,32,0.2806807709516379,0.0001561962879473,0.9402685165405272
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,921,100%,32,0.2806807709516379,0.0001561962879473,0.935638189315796
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2885,100%,32,0.2806807709516379,0.0001561962879473,0.9274752140045166
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1833,100%,32,0.2806807709516379,0.0001561962879473,0.9409592151641846
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2434,100%,32,0.2806807709516379,0.0001561962879473,0.942594826221466
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4523,100%,32,0.2806807709516379,0.0001561962879473,0.9272408485412598
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1074,100%,32,0.2806807709516379,0.0001561962879473,0.9365390539169312
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2695,100%,32,0.2806807709516379,0.0001561962879473,0.9388547539711
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3339,100%,32,0.2806807709516379,0.0001561962879473,0.9340709447860718
+pv4ger_seg,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,778,100%,32,0.2806807709516379,0.0001561962879473,0.9414107799530028
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,391,100%,8,0.1872029616850694,0.0001583623070717,0.8811582922935486
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,125,100%,8,0.1872029616850694,0.0001583623070717,0.7500208616256714
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1471,100%,8,0.1872029616850694,0.0001583623070717,0.7552796006202698
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,185,100%,8,0.1872029616850694,0.0001583623070717,0.7548510432243347
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3220,100%,8,0.1872029616850694,0.0001583623070717,0.8831520676612854
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1477,100%,8,0.1872029616850694,0.0001583623070717,0.7554706335067749
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,27,100%,8,0.1872029616850694,0.0001583623070717,0.8891997337341309
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3893,100%,8,0.1872029616850694,0.0001583623070717,0.7583714723587036
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3109,100%,8,0.1872029616850694,0.0001583623070717,0.74748694896698
+cashew,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1561,100%,8,0.1872029616850694,0.0001583623070717,0.7510426640510559
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,339,100%,8,0.0958805157666471,0.0006669639988907,0.567497730255127
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1119,100%,8,0.0958805157666471,0.0006669639988907,0.5694387555122375
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1598,100%,8,0.0958805157666471,0.0006669639988907,0.5681214928627014
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,59,100%,8,0.0958805157666471,0.0006669639988907,0.5635424852371216
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,299,100%,8,0.0958805157666471,0.0006669639988907,0.5690293312072754
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3969,100%,8,0.0958805157666471,0.0006669639988907,0.5734402537345886
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1335,100%,8,0.0958805157666471,0.0006669639988907,0.5750075578689575
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1205,100%,8,0.0958805157666471,0.0006669639988907,0.5757657289505005
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1984,100%,8,0.0958805157666471,0.0006669639988907,0.5712120532989502
+neontree,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1387,100%,8,0.0958805157666471,0.0006669639988907,0.5796757936477661
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2986,100%,32,0.2406274751665054,0.0007374796618441,0.8232717514038086
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3692,100%,32,0.2406274751665054,0.0007374796618441,0.8008925914764404
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2829,100%,32,0.2406274751665054,0.0007374796618441,0.8172112107276917
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,3583,100%,32,0.2406274751665054,0.0007374796618441,0.8212674856185913
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4245,100%,32,0.2406274751665054,0.0007374796618441,0.8171231746673584
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4544,100%,32,0.2406274751665054,0.0007374796618441,0.8219597339630127
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,1204,100%,32,0.2406274751665054,0.0007374796618441,0.8255108594894409
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,15,100%,32,0.2406274751665054,0.0007374796618441,0.8165488243103027
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,4111,100%,32,0.2406274751665054,0.0007374796618441,0.8116637468338013
+nz_cattle,Multiclass_Jaccard_Index,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_smp_unet,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,smp_Unet,optimized,50,16,2099,100%,32,0.2406274751665054,0.0007374796618441,0.7985596060752869
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4565,100%,32,0.0483818466583478,0.0009007718997571,0.9809809923171996
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4322,100%,32,0.0483818466583478,0.0009007718997571,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4148,100%,32,0.0483818466583478,0.0009007718997571,0.9819819927215576
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,16,100%,32,0.0483818466583478,0.0009007718997571,0.977977991104126
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3799,100%,32,0.0483818466583478,0.0009007718997571,0.9679679870605468
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4370,100%,32,0.0483818466583478,0.0009007718997571,0.9719719886779784
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4217,100%,32,0.0483818466583478,0.0009007718997571,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2748,100%,32,0.0483818466583478,0.0009007718997571,0.978978991508484
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4964,100%,32,0.0483818466583478,0.0009007718997571,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,145,100%,32,0.0483818466583478,0.0009007718997571,0.9589589834213256
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1582,100%,16,0.1420142212351907,6.484318042499172e-05,0.517241358757019
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3140,100%,16,0.1420142212351907,6.484318042499172e-05,0.568965494632721
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1849,100%,16,0.1420142212351907,6.484318042499172e-05,0.5557809472084045
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1966,100%,16,0.1420142212351907,6.484318042499172e-05,0.5030425786972046
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3231,100%,16,0.1420142212351907,6.484318042499172e-05,0.5425963401794434
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2190,100%,16,0.1420142212351907,6.484318042499172e-05,0.5486815571784973
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4762,100%,16,0.1420142212351907,6.484318042499172e-05,0.5679513216018677
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3672,100%,16,0.1420142212351907,6.484318042499172e-05,0.5415821671485901
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1329,100%,16,0.1420142212351907,6.484318042499172e-05,0.5446247458457947
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1399,100%,16,0.1420142212351907,6.484318042499172e-05,0.5334685444831848
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3310,100%,32,0.0016925260317968,6.455143990010064e-05,0.9869869947433472
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,535,100%,32,0.0016925260317968,6.455143990010064e-05,0.9819819927215576
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,562,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1753,100%,32,0.0016925260317968,6.455143990010064e-05,0.9849849939346312
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2622,100%,32,0.0016925260317968,6.455143990010064e-05,0.9649649858474731
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,814,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,237,100%,32,0.0016925260317968,6.455143990010064e-05,0.9839839935302734
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1307,100%,32,0.0016925260317968,6.455143990010064e-05,0.9859859943389891
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4435,100%,32,0.0016925260317968,6.455143990010064e-05,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4089,100%,32,0.0016925260317968,6.455143990010064e-05,0.9839839935302734
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4838,100%,32,0.2103480107442391,6.471079283583433e-05,0.6599027514457703
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,713,100%,32,0.2103480107442391,6.471079283583433e-05,0.6716534495353699
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4468,100%,32,0.2103480107442391,6.471079283583433e-05,0.6648851037025452
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,840,100%,32,0.2103480107442391,6.471079283583433e-05,0.6675868034362793
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1144,100%,32,0.2103480107442391,6.471079283583433e-05,0.6821687817573547
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1829,100%,32,0.2103480107442391,6.471079283583433e-05,0.6789093017578125
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2007,100%,32,0.2103480107442391,6.471079283583433e-05,0.6702918410301208
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2254,100%,32,0.2103480107442391,6.471079283583433e-05,0.6647154688835144
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,205,100%,32,0.2103480107442391,6.471079283583433e-05,0.6651250123977661
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,913,100%,32,0.2103480107442391,6.471079283583433e-05,0.6596580743789673
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3130,100%,32,0.290578170946364,8.388751435377213e-05,0.9739999771118164
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1220,100%,32,0.290578170946364,8.388751435377213e-05,0.9629999995231628
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1188,100%,32,0.290578170946364,8.388751435377213e-05,0.962000012397766
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2923,100%,32,0.290578170946364,8.388751435377213e-05,0.9700000286102296
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2033,100%,32,0.290578170946364,8.388751435377213e-05,0.968999981880188
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,445,100%,32,0.290578170946364,8.388751435377213e-05,0.972000002861023
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2729,100%,32,0.290578170946364,8.388751435377213e-05,0.9679999947547911
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3499,100%,32,0.290578170946364,8.388751435377213e-05,0.962000012397766
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1144,100%,32,0.290578170946364,8.388751435377213e-05,0.9679999947547911
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,2902,100%,32,0.290578170946364,8.388751435377213e-05,0.9739999771118164
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,707,100%,16,0.3941167807260976,0.0002822095144255,0.4521651566028595
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3382,100%,16,0.3941167807260976,0.0002822095144255,0.4692849814891815
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4829,100%,16,0.3941167807260976,0.0002822095144255,0.448136955499649
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4496,100%,16,0.3941167807260976,0.0002822095144255,0.4360523521900177
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,4976,100%,16,0.3941167807260976,0.0002822095144255,0.4189325273036957
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1421,100%,16,0.3941167807260976,0.0002822095144255,0.4269889295101166
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3807,100%,16,0.3941167807260976,0.0002822095144255,0.4390735030174255
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3103,100%,16,0.3941167807260976,0.0002822095144255,0.4491440057754516
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,1716,100%,16,0.3941167807260976,0.0002822095144255,0.4471299052238464
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_dino_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_dino,IdentityDecoder,optimized,50,16,3421,100%,16,0.3941167807260976,0.0002822095144255,0.4290030300617218
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1006,100%,16,0.0233859235441379,0.0008743862145496,0.9689689874649048
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,855,100%,16,0.0233859235441379,0.0008743862145496,0.9759759902954102
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,274,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3967,100%,16,0.0233859235441379,0.0008743862145496,0.966966986656189
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1261,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3907,100%,16,0.0233859235441379,0.0008743862145496,0.977977991104126
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,507,100%,16,0.0233859235441379,0.0008743862145496,0.9699699878692628
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4890,100%,16,0.0233859235441379,0.0008743862145496,0.9739739894866944
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,821,100%,16,0.0233859235441379,0.0008743862145496,0.9729729890823364
+pv4ger,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,897,100%,16,0.0233859235441379,0.0008743862145496,0.9759759902954102
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2709,100%,8,0.2143980952203471,7.678537962381587e-05,0.546653151512146
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4385,100%,8,0.2143980952203471,7.678537962381587e-05,0.523326575756073
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2334,100%,8,0.2143980952203471,7.678537962381587e-05,0.5537525415420532
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2296,100%,8,0.2143980952203471,7.678537962381587e-05,0.563894510269165
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2335,100%,8,0.2143980952203471,7.678537962381587e-05,0.540567934513092
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4220,100%,8,0.2143980952203471,7.678537962381587e-05,0.5567951202392578
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,600,100%,8,0.2143980952203471,7.678537962381587e-05,0.5415821671485901
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4085,100%,8,0.2143980952203471,7.678537962381587e-05,0.5081135630607605
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3605,100%,8,0.2143980952203471,7.678537962381587e-05,0.4898580014705658
+so2sat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4851,100%,8,0.2143980952203471,7.678537962381587e-05,0.5578093528747559
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2466,100%,32,0.0241049339778712,0.0006317522129654,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2541,100%,32,0.0241049339778712,0.0006317522129654,0.9829829931259156
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2246,100%,32,0.0241049339778712,0.0006317522129654,0.978978991508484
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,132,100%,32,0.0241049339778712,0.0006317522129654,0.9689689874649048
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1691,100%,32,0.0241049339778712,0.0006317522129654,0.9729729890823364
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1696,100%,32,0.0241049339778712,0.0006317522129654,0.9799799919128418
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2224,100%,32,0.0241049339778712,0.0006317522129654,0.9799799919128418
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4222,100%,32,0.0241049339778712,0.0006317522129654,0.9809809923171996
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1077,100%,32,0.0241049339778712,0.0006317522129654,0.9739739894866944
+brick_kiln,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3614,100%,32,0.0241049339778712,0.0006317522129654,0.95695698261261
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4838,100%,32,0.0006609967274643,0.0001244735178777,0.6715825200080872
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,713,100%,32,0.0006609967274643,0.0001244735178777,0.6624463200569153
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4468,100%,32,0.0006609967274643,0.0001244735178777,0.6741133332252502
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,840,100%,32,0.0006609967274643,0.0001244735178777,0.6756007671356201
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1144,100%,32,0.0006609967274643,0.0001244735178777,0.6727983355522156
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1829,100%,32,0.0006609967274643,0.0001244735178777,0.6618573069572449
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2007,100%,32,0.0006609967274643,0.0001244735178777,0.6743751168251038
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2254,100%,32,0.0006609967274643,0.0001244735178777,0.6706086993217468
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,205,100%,32,0.0006609967274643,0.0001244735178777,0.6765933632850647
+big_earth_net,Multilabel_F1_Score,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,913,100%,32,0.0006609967274643,0.0001244735178777,0.6776209473609924
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1698,100%,16,0.1849003044425631,0.0001063634870237,0.9539999961853028
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3146,100%,16,0.1849003044425631,0.0001063634870237,0.9539999961853028
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4144,100%,16,0.1849003044425631,0.0001063634870237,0.9639999866485596
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3064,100%,16,0.1849003044425631,0.0001063634870237,0.9660000205039978
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2803,100%,16,0.1849003044425631,0.0001063634870237,0.9430000185966492
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1624,100%,16,0.1849003044425631,0.0001063634870237,0.9760000109672546
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,614,100%,16,0.1849003044425631,0.0001063634870237,0.9559999704360962
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2735,100%,16,0.1849003044425631,0.0001063634870237,0.9580000042915344
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2931,100%,16,0.1849003044425631,0.0001063634870237,0.9639999866485596
+eurosat,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2808,100%,16,0.1849003044425631,0.0001063634870237,0.9480000138282776
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,272,100%,32,0.0028788214595454,6.027942222485105e-05,0.4803625345230102
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,719,100%,32,0.0028788214595454,6.027942222485105e-05,0.5075528621673584
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,2115,100%,32,0.0028788214595454,6.027942222485105e-05,0.5629405975341797
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,4503,100%,32,0.0028788214595454,6.027942222485105e-05,0.5156092643737793
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,940,100%,32,0.0028788214595454,6.027942222485105e-05,0.5276938676834106
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3361,100%,32,0.0028788214595454,6.027942222485105e-05,0.5236656665802002
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,543,100%,32,0.0028788214595454,6.027942222485105e-05,0.5085599422454834
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1667,100%,32,0.0028788214595454,6.027942222485105e-05,0.5357502698898315
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,1312,100%,32,0.0028788214595454,6.027942222485105e-05,0.52064448595047
+forestnet,Overall_Accuracy,early_stopping_50_data_100_perc_ssl4eos12_resnet50_sentinel2_all_decur_true,1.00x train,ssl4eos12_resnet50_sentinel2_all_decur,IdentityDecoder,optimized,50,16,3415,100%,32,0.0028788214595454,6.027942222485105e-05,0.5287008881568909

tests/test_compile_results.py

@@ -0,0 +1,83 @@
+import pytest
+from pathlib import Path
+import pandas as pd
+from typing import Dict
+import pickle
+from utils.compile_results import *
+from utils.constants import NORM_BASE_SUBMISSION
+
+root = Path(__file__).parent.resolve()
+root = str(root)
+
+SUBMISSION_NAMES = ['test_submission_1']
+MODEL_NAMES = ["ssl4eos12_resnet50_sentinel2_all_decur","ssl4eos12_resnet50_sentinel2_all_dino"]
+
+
+@pytest.fixture
+def expected_output():
+    with open(f"{root}/resources/outputs/test_output.pkl", 'rb') as handle:
+        expected_results = pickle.load(handle)
+    return expected_results
+
+
+class TestLoading:
+    all_submission_results, all_model_names, all_submissions = load_results(folder=f"{root}/resources/inputs")    
+    def test_submission_results(self):
+        for _, value in self.all_submission_results.items():
+            assert "results" in value
+            assert type(value["results"]) == pd.DataFrame
+
+            columns_to_be_added = {"# params", "Model", "Config Settings"}
+            existing_columns = set(value["results"].columns)
+            assert columns_to_be_added.issubset(existing_columns)
+
+    def test_model_names(self):
+        assert sorted(self.all_model_names) == sorted(MODEL_NAMES)
+
+    def test_submission_names(self):
+        assert sorted(self.all_submissions) == SUBMISSION_NAMES
+ 
+
+
+class TestComputeResults:
+    #compute output
+    all_submission_results, all_model_names, all_submissions = load_results(folder=f"{root}/resources/inputs")    
+    benchmark_name = f"leaderboard_{NORM_BASE_SUBMISSION}_main"  
+    all_iqms = compute_all_iqms(all_submission_results =  all_submission_results,
+                                benchmark_name = benchmark_name
+                                )
+    overall_performance_tables = get_overall_performance_table(all_submission_results=all_submission_results, 
+                                                                all_iqms=all_iqms)
+    performance_by_dimension_tables = get_performance_by_dimension_table(all_submission_results=all_submission_results, 
+                                                                         all_iqms=all_iqms)
+    datasets_tables = get_datasets_tables(all_submission_results=all_submission_results, 
+                                            all_iqms=all_iqms)
+    
+    def test_compute_all_iqms(self, expected_output):
+        assert sorted(self.all_iqms.keys()) == sorted(self.all_submission_results.keys())
+        assert "overall_performance_tables" in expected_output
+
+        for submission, submission_value in self.all_iqms.items():
+            assert sorted(submission_value.keys()) == sorted(expected_output["all_iqms"][submission].keys())#sorted(ALL_TABLES)
+
+            for table_name, table in submission_value.items():
+                #print(f"{table_name}: {type(table) == pd.DataFrame}")
+                #print(f"table.columns: {table.columns}")
+                #print(f"expected_output['all_iqms'][submission][table_name]{expected_output['all_iqms'][submission][table_name].columns}")
+                assert type(table) == pd.DataFrame
+                #assert table.equals(expected_output["all_iqms"][submission][table_name])
+
+
+    
+    def test_raw_values(self):
+        assert "raw" in self.overall_performance_tables
+        #dataset values
+        #overall values
+        #dimension values
+
+    def test_normalized_values(self):
+        assert "normalized" in self.overall_performance_tables
+        #dataset values
+        #overall values
+        #dimension values
+        pass

utils/__init__.py

@@ -0,0 +1 @@
+from . import *

utils/about_page.txt

@@ -0,0 +1,38 @@
+### 1. Create New Submission Directory
+Create a new folder in the `new_submission` top directory:
+```bash
+geobench_leaderboard/
+└── new_submission/
+    ├── results_and_parameters.csv
+    ├── additional_info.json
+```
+
+### 2. Add Results and Parameters Details
+
+Add a CSV file (`results_and_parameters.csv`) with the columns below. Please note that if terratorch-iterate is used for experiments, this table may be created automatically upon completion of an experiment. Please see https://github.com/The-AI-Alliance/GEO-Bench-Leaderboard for more:
+ - `backbone`: backbone used for experiment, (e.g. Prithvi-EO-V2 600M)
+ - `dataset`: some or all of the GEO-bench datasets. Please see Info page to learn more.
+ - `Metric`: the type of metric used for evaluation. Depending on the dataset, this may be one of the following: `Overall_Accuracy`, `Multilabel_F1_Score`, `Multiclass_Jaccard_Index`
+ - `experiment_name`: if terratorch-iterate used, this will the experiment_name used in mlflow. Otherwise, a unique name may be used for all results relating to a single backbone
+ - `batch_size_selection`: denotes whether the batch size was fixed during hyperparameter optimization. May be `fixed` or `optimized`
+ - `early_stop_patience`: early stopping patience using for trainer
+ - `n_trials`: number of trials used for hyperparameter optimization
+ - `Seed`: random seed used for repeated experiment. 10 random seeds must be used for each 
+ - `batch_size`: batch size used for repeated experiments for each backbone/dataset combination.
+ - `weight_decay`: weight decay experiments for each backbone/dataset combination.
+ - `lr`: learning rate used for repeated experiments for each backbone/dataset combination. Obtained from hyperparameter optimization (HPO)
+ - `test metric`: metric obtained from running backbone on the dataset during repeated experiment. Please see Info page to learn more. 
+
+
+
+### 3. Add Additional Information
+Create a JSON file (`additional_info.json`) with information about your submission and any new models that will be included.
+The JSON file MUST have the same file name and contain the same keys as the `examples/additional_info.json` file. 
+
+
+### 4. Submit PR
+
+1. Fork the repository
+2. Add your results following the structure above and in the PR comments add more details about your submission
+3. Create a pull request to main
+

utils/compile_results.py

@@ -0,0 +1,534 @@
+import json
+import re
+import os
+
+import pandas as pd
+import numpy as np
+import pickle
+
+from urllib.parse import quote
+from pathlib import Path
+import re
+import html
+from typing import Dict, Any
+from scipy.stats import sem
+
+from utils.constants import (NORM_BASE_SUBMISSION, DATASETS, DIGITS_FOR_VALUES, DIGITS_FOR_ERRORS,
+                             DIMENSIONS, COLUMN_ORDER, MODEL_INFO_FILE, RESULTS_DIR)
+from utils import compute_tools
+
+
+
+def load_results(folder: str = RESULTS_DIR,
+                items_to_ignore: list = ["__pycache__", "compiled.pkl", ".DS_Store"]
+                ):
+    """
+    loads results from results folder.
+
+    Args:
+        folder: folder containing results
+        items_to_ignore: list of items in results folder to ignore
+    """
+    #read model info
+    with open(MODEL_INFO_FILE) as f:
+        model_info = json.load(f)
+    model_size = model_info["MODEL_SIZE"]
+    backbone_names = model_info["BACKBONE_NAMES"]
+
+    #read submission info
+    all_submissions = os.listdir(folder)
+    for item in items_to_ignore:
+        if item in all_submissions: all_submissions.remove(item)
+
+    all_submission_results = {}
+    #TODO: add some info to json files and read here also
+    all_full_ft_model_names = []
+    all_frozen_model_names = []
+    all_submission_results["frozen"] = {}
+    all_submission_results["full_ft"] = {}
+    for submission in all_submissions:        
+        combined_results = pd.read_csv(f"{folder}/{submission}/results_and_parameters.csv")
+        combined_results = combined_results.drop(["index"], errors='ignore')
+        try:
+            frozen_or_full_ft = combined_results["frozen_or_full_ft"][0]    
+        except KeyError as e:
+            KeyError(f"{combined_results=}")
+        all_submission_results[frozen_or_full_ft][submission] = {}
+
+        combined_results["# params"]  = combined_results.apply(lambda row: model_size[row.backbone], axis=1)
+        combined_results["Model"]  = combined_results.apply(lambda row: backbone_names[row.backbone], axis=1)
+        combined_results["Config Settings"]  = combined_results.apply(lambda row: get_config_setting_string(row), axis=1)
+
+        #TODO: read json info
+        all_backbones = list(set(combined_results["backbone"].tolist()))
+        all_submission_results[frozen_or_full_ft][submission]["results"] = combined_results
+        all_submission_results[frozen_or_full_ft][submission]["all_backbones"] = all_backbones
+        
+        config_settings = combined_results[["early_stop_patience", "decoder", "n_trials", "data_percentages", "batch_size_selection"]].iloc[0]
+        config_settings = config_settings.replace("early_stopping_50", "50").replace("n_trials_16", "16").replace("data_100_perc", "100") 
+        all_submission_results[frozen_or_full_ft][submission]["config_info"] = config_settings
+        
+        #all_submission_results[submission]["json_info"] = json_info
+        if frozen_or_full_ft =="frozen":
+            all_frozen_model_names.extend(all_backbones)
+        else:
+            all_full_ft_model_names.extend(all_backbones)
+
+    all_frozen_model_names = list(set(all_frozen_model_names))
+    all_full_ft_model_names = list(set(all_full_ft_model_names))
+    all_model_names = {"full_ft": all_full_ft_model_names, "frozen": all_frozen_model_names}
+    return all_submission_results, all_model_names, all_submissions
+
+
+
+def compute_all_iqms(
+                all_submission_results: dict,
+                benchmark_name: str,
+                dataset_group_keys:list =["backbone", "dataset"],
+                overall_group_keys:list = ["backbone"],
+                metric:str ="test metric",
+                ) -> Dict:
+    """
+    - reads combined results from repeated seeds for multiple models
+    - computes the raw and normalized IQM by dataset for each model by task type
+    - computes the raw and normalized overall IQM across multiple datasets in each each task type
+    
+    Args:
+        all_submission_results: dict containing all results
+        benchmark_name: name of normalizer file to be used
+        dataset_group_keys: grouping for computing dataset IQM
+        overall_group_keys: grouping for computing overall IQM
+        metric: the column containing scores/values in the combined results tables
+    """
+    output = {}
+    for submission in all_submission_results:
+        output[submission] = {}
+        print(f'\n\n\n{submission=}')
+        submission_backbones = all_submission_results[submission]["all_backbones"]
+
+        #TODO: remove
+        partition_name =  "0.10x train" if "data_10_perc" in submission else "1.00x train" 
+        submission_results = all_submission_results[submission]["results"]
+        if not "partition name" in list(submission_results.columns):
+            submission_results["partition name"] = partition_name 
+        submission_results["partition name"] = partition_name 
+
+        #get raw values per dataset
+        series = submission_results.groupby(dataset_group_keys)[metric].apply(np.mean)
+        raw_per_dataset = series.to_frame().reset_index()
+        raw_per_dataset = raw_per_dataset.drop(columns=["partition name"], errors='ignore') 
+        included_datsets = [d for d in DATASETS if d in set(raw_per_dataset["dataset"])]
+        raw_per_dataset_final = pd.DataFrame(columns=["backbone"] + included_datsets)
+        
+        #get raw errors per dataset
+        series = submission_results.groupby(dataset_group_keys)[metric].apply(sem)
+        raw_per_dataset_err = series.to_frame().reset_index()
+        raw_per_dataset_err = raw_per_dataset_err.drop(columns=["partition name"], errors='ignore') 
+        raw_per_dataset_final_err = pd.DataFrame(columns=["backbone"] + included_datsets)
+       
+        #rearrange
+        for backbone in submission_backbones:
+            #get values
+            data = raw_per_dataset.loc[raw_per_dataset["backbone"] == backbone]
+            data = data.drop(columns=["backbone"]).rename(columns={metric: backbone, "dataset": "backbone"})
+            data = data.set_index(['backbone']).T.reset_index()
+            data = data.rename(columns={"index": "backbone"})
+            try:
+                data = data.loc[:, ["backbone"] + included_datsets]
+            except KeyError as e:
+                print(f'{backbone} {e=}')
+                continue
+
+            raw_per_dataset_final = data.copy() if len(raw_per_dataset_final.index)==0 else pd.concat([raw_per_dataset_final, data], ignore_index=True)
+
+            #get errors
+            data_err = raw_per_dataset_err.loc[raw_per_dataset_err["backbone"] == backbone]
+            data_err = data_err.drop(columns=["backbone"]).rename(columns={metric: backbone, "dataset": "backbone"})
+            data_err = data_err.set_index(['backbone']).T.reset_index()
+            data_err = data_err.rename(columns={"index": "backbone"})
+            data_err = data_err.loc[:, ["backbone"] + included_datsets]
+            raw_per_dataset_final_err = data_err.copy() if len(raw_per_dataset_final_err.index)==0 else pd.concat([raw_per_dataset_final_err, data_err], ignore_index=True)
+
+        raw_per_dataset_final = raw_per_dataset_final.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
+        raw_per_dataset_final_err = raw_per_dataset_final_err.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
+        raw_per_dataset_final = raw_per_dataset_final.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
+        raw_per_dataset_final_err = raw_per_dataset_final_err.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
+
+        #normalize results
+        normalizer = compute_tools.load_normalizer(benchmark_name=benchmark_name)
+        new_metric = normalizer.normalize_data_frame(df=submission_results, metric=metric)
+        
+        #get normalized values  per dataset
+        series = submission_results.groupby(dataset_group_keys)[new_metric].apply(compute_tools.iqm)
+        normalized_per_dataset = series.to_frame().reset_index()
+        normalized_per_dataset = normalized_per_dataset.drop(columns=["partition name"], errors='ignore') 
+        included_datsets = [d for d in DATASETS if d in set(normalized_per_dataset["dataset"])]
+        normalized_per_dataset_final = pd.DataFrame(columns=["backbone"] + included_datsets)
+
+        #get normalized errors per dataset
+        series = submission_results.groupby(dataset_group_keys)[new_metric].apply(compute_tools.trimmed_sem)
+        normalized_per_dataset_err = series.to_frame().reset_index()
+        normalized_per_dataset_err = normalized_per_dataset_err.drop(columns=["partition name"], errors='ignore') 
+        normalized_per_dataset_final_err = pd.DataFrame(columns=["backbone"] + included_datsets)
+
+        #rearrange
+        for backbone in submission_backbones:
+            #get values
+            data = normalized_per_dataset.loc[normalized_per_dataset["backbone"] == backbone]
+            data = data.drop(columns=["backbone"]).rename(columns={new_metric: backbone, "dataset": "backbone"})
+            data = data.set_index(['backbone']).T.reset_index()
+            data = data.rename(columns={"index": "backbone"})
+            try:
+                data = data.loc[:, ["backbone"] + included_datsets]
+            except KeyError as e:
+                print(f'{backbone} {e=}')
+                continue
+            normalized_per_dataset_final = data.copy() if len(normalized_per_dataset_final.index)==0 else pd.concat([normalized_per_dataset_final, data], ignore_index=True)
+
+            #get errors
+            data_err = normalized_per_dataset_err.loc[normalized_per_dataset["backbone"] == backbone]
+            data_err = data_err.drop(columns=["backbone"]).rename(columns={new_metric: backbone, "dataset": "backbone"})
+            data_err = data_err.set_index(['backbone']).T.reset_index()
+            data_err = data_err.rename(columns={"index": "backbone"})
+            data_err = data_err.loc[:, ["backbone"] + included_datsets]
+            normalized_per_dataset_final_err = data_err.copy() if len(normalized_per_dataset_final_err.index)==0 else pd.concat([normalized_per_dataset_final_err, data_err], ignore_index=True)
+
+        normalized_per_dataset_final = normalized_per_dataset_final.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
+        normalized_per_dataset_final_err = normalized_per_dataset_final_err.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
+        normalized_per_dataset_final =normalized_per_dataset_final.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
+        normalized_per_dataset_final_err =normalized_per_dataset_final_err.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
+
+        #get normalized values by dimension
+        normalized_overall = pd.DataFrame(columns=["backbone"])
+        normalized_overall_std_err = pd.DataFrame(columns=["backbone"])
+        submission_dimensions = []
+        for dimension in DIMENSIONS:
+            dimension_data = submission_results.loc[submission_results["dataset"].isin(DIMENSIONS[dimension])].copy()
+            dimension_datasets = sorted(set(dimension_data["dataset"]))
+            dimension_backbones = sorted(set(dimension_data["backbone"]))
+            exclude_backbone = []
+            for backbone in dimension_backbones:
+                backbone_datasets = dimension_data.loc[dimension_data["backbone"] == backbone]["dataset"].tolist()
+                if set(backbone_datasets) != set(dimension_datasets):
+                    #if backbone is missing datasets, drop from table
+                    exclude_backbone.append(backbone)
+                    
+            dimension_datasets = [True if d in dimension_datasets else False for d in DIMENSIONS[dimension]]
+            # dimension_data = dimension_data[~dimension_data["backbone"].isin(exclude_backbone)]
+            if all(dimension_datasets): 
+                submission_dimensions.append(dimension)
+                
+                #get values
+                normalized_iqms_dimension = compute_tools.bootstrap_iqm_aggregate(dimension_data, metric= new_metric)
+                series = normalized_iqms_dimension.groupby(overall_group_keys)[new_metric].apply(np.mean)
+                normalized_iqms_dimension = series.to_frame().reset_index()
+                normalized_iqms_dimension = normalized_iqms_dimension.rename(columns={new_metric: dimension})
+                normalized_iqms_dimension.loc[normalized_iqms_dimension["backbone"].isin(exclude_backbone), dimension, ] = np.nan
+
+            
+                #get errors
+                normalized_dimension_std_err = compute_tools.bootstrap_iqm_aggregate(dimension_data, metric=new_metric)
+                series = normalized_dimension_std_err.groupby(["backbone"])[new_metric].apply(sem)
+                # series = submission_results.loc[submission_results["dataset"].isin(DIMENSIONS[dimension])].copy()
+                # series = series[~series["dataset"].isin(exclude_backbone)]
+                # series = series.groupby(overall_group_keys)[new_metric].apply(sem)
+                normalized_dimension_std_err = series.to_frame().reset_index()  
+                normalized_dimension_std_err = normalized_dimension_std_err.drop(columns=["partition name"], errors='ignore') 
+                normalized_dimension_std_err = normalized_dimension_std_err.rename(columns={new_metric: dimension})
+                normalized_dimension_std_err.loc[normalized_dimension_std_err["backbone"].isin(exclude_backbone), dimension] = np.nan
+                # series = dimension_data.groupby(overall_group_keys)[new_metric].apply(sem)
+                # normalized_dimension_std_err = series.to_frame().reset_index()
+                # normalized_dimension_std_err = normalized_dimension_std_err.rename(columns={new_metric: dimension})
+            else:
+                normalized_iqms_dimension = pd.DataFrame({
+                    "backbone": submission_backbones,
+                    dimension: [np.nan] * len(submission_backbones),
+                })
+                normalized_dimension_std_err = pd.DataFrame({
+                    "backbone": submission_backbones,
+                    dimension: [np.nan] * len(submission_backbones),
+                })
+                
+            normalized_iqms_dimension.sort_values(by=['backbone'], inplace=True)
+            normalized_dimension_std_err.sort_values(by=['backbone'], inplace=True)
+            normalized_overall = normalized_iqms_dimension.copy() if len(normalized_overall.index)==0 else normalized_overall.merge(normalized_iqms_dimension, how="left", on="backbone")
+            normalized_overall_std_err = normalized_dimension_std_err.copy() if len(normalized_overall_std_err.index)==0 else normalized_overall_std_err.merge(normalized_dimension_std_err, how="left", on="backbone")
+
+        output[submission]["raw_per_dataset"] = raw_per_dataset_final
+        output[submission]["normalized_per_dataset"] = normalized_per_dataset_final
+        output[submission]["normalized_overall"] = normalized_overall
+        output[submission]["raw_per_dataset_err"] = raw_per_dataset_final_err
+        output[submission]["normalized_per_dataset_err"] = normalized_per_dataset_final_err
+        output[submission]["normalized_overall_err"] = normalized_overall_std_err
+        output[submission]["submission_dimensions"] = submission_dimensions
+    return output
+
+
+
+def format_values(x):
+    x = x*100
+    x = round(x,1)
+    return x
+
+
+def format_errors(x):
+    x = x*100
+    x = round(x,1)
+    return x
+
+
+def get_config_setting_string(row) -> str:
+    config_settings = f"""
+                        Early Stop Patience: {row.early_stop_patience} /
+                        Decoder: {row.decoder} /
+                        # trials: {row.n_trials} /
+                        Data : {row.data_percentages}% /
+                        Batch Size Selection: {row.batch_size_selection}
+                        """
+    config_settings = config_settings.replace("early_stopping_50", "50").replace("n_trials_16", "16").replace("data_100_perc", "100") 
+    return config_settings
+
+
+def get_overall_performance_table(all_submission_results: dict, 
+                                all_iqms: dict
+                                ) -> Dict:
+    """
+    create tables for 'Aggregated Performance' page.
+
+    Args:
+        all_submission_results: dict containing all results
+        all_iqms: dict containing all computed results
+
+    """
+    output = {}
+    result_type = ["normalized"]
+    for value in result_type:
+        all_tables = []
+        all_tables_err = []
+        for submission in all_submission_results:
+            #get results
+            submission_data = all_iqms[submission][f"{value}_overall"].copy()
+            submission_data["Model"] = "-"
+            submission_data["# params"] = "-"
+            submission_data["submission"] = submission
+
+            submission_data_err = all_iqms[submission][f"{value}_overall_err"].copy()
+            submission_data_err["Config Settings"] = "-"
+            submission_data_err["Model"] = "-"
+            submission_data_err["# params"] = "-"
+            submission_data_err["submission"] = submission
+
+            #get parameters
+            parameters = all_submission_results[submission]["results"]
+            for backbone in all_submission_results[submission]["all_backbones"]:
+                submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+
+                submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+            all_tables.append(submission_data)
+            all_tables_err.append(submission_data_err)
+            print(f'\n\n\n {submission} {value} {submission_data[["Core", "Detection (Object/Instance)", "Model", "submission"]].head(50)=}')
+
+        all_tables = pd.concat(all_tables)
+        all_tables_err = pd.concat(all_tables_err)
+        all_tables.loc[:, COLUMN_ORDER[value]["overall_table"]] = all_tables[COLUMN_ORDER[value]["overall_table"]].round(DIGITS_FOR_VALUES).apply(lambda series: series.apply(format_values))    
+        all_tables_err.loc[:, COLUMN_ORDER[value]["overall_table"]]= all_tables_err[COLUMN_ORDER[value]["overall_table"]].round(DIGITS_FOR_ERRORS).apply(lambda series: series.apply(format_errors))  
+        all_tables = all_tables[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["overall_table"]]
+        all_tables_err = all_tables_err[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["overall_table"]]
+        for col in COLUMN_ORDER[value]["overall_table"]:
+            new_column = f"{col}"
+            all_tables = all_tables.rename(columns={col: new_column})
+            all_tables_err = all_tables_err.rename(columns={col: new_column})
+        output[value] = all_tables
+        output[f"{value}_err"] = all_tables_err
+    return output
+
+        
+def get_performance_by_dimension_table(all_submission_results: dict, 
+                                       all_iqms: dict
+                                        ) -> Dict:
+    """
+    create tables for 'Capabilities' page.
+
+    Args:
+        all_submission_results: dict containing all results
+        all_iqms: dict containing all computed results
+
+    """
+    output = {}
+    result_type = ["normalized"]
+    for value in result_type:
+        all_tables = {}
+        all_tables_err = {}
+        for dimension in DIMENSIONS:
+            dimension_tables = []
+            dimension_tables_err = []
+            for submission in all_submission_results:
+                #get results
+                submission_data = all_iqms[submission][f"{value}_per_dataset"][DIMENSIONS[dimension]+["backbone"]].copy()
+                dimension_results = all_iqms[submission][f"{value}_overall"][[dimension]+["backbone"]].copy()
+                submission_data = submission_data.merge(dimension_results, how="left", on="backbone")
+                submission_data["Model"] = "-"
+                submission_data["# params"] = "-"
+                submission_data["submission"] = submission
+
+                submission_data_err = all_iqms[submission][f"{value}_per_dataset_err"][DIMENSIONS[dimension]+["backbone"]].copy()
+                dimension_results_err = all_iqms[submission][f"{value}_overall_err"][[dimension]+["backbone"]].copy()
+                submission_data_err = submission_data_err.merge(dimension_results_err, how="left", on="backbone")
+                submission_data_err["Model"] = "-"
+                submission_data_err["# params"] = "-"
+                submission_data_err["submission"] = submission
+
+                #get parameters
+                parameters = all_submission_results[submission]["results"]
+                for backbone in all_submission_results[submission]["all_backbones"]:
+                    submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                    submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+
+                    submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                    submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+                dimension_tables.append(submission_data)
+                dimension_tables_err.append(submission_data_err)
+                # print(f'\n\n\n {submission} {dimension} {submission_data[[dimension, "Model", "submission"]].head(50)=}')
+
+            dimension_tables = pd.concat(dimension_tables)
+            dimension_tables.loc[:, DIMENSIONS[dimension]] = dimension_tables[DIMENSIONS[dimension]].round(DIGITS_FOR_VALUES).apply(lambda series: series.apply(format_values))        
+            dimension_tables.loc[:, dimension] = dimension_tables[dimension].round(DIGITS_FOR_VALUES).apply(format_values)
+            dimension_tables = dimension_tables[COLUMN_ORDER["all_tables"] + [dimension] + COLUMN_ORDER[value]["dimension_tables"]  + DIMENSIONS[dimension]]
+            new_column = f"{dimension}"
+            dimension_tables = dimension_tables.rename(columns={dimension: new_column})
+            all_tables[dimension] = dimension_tables
+
+            dimension_tables_err = pd.concat(dimension_tables_err)
+            dimension_tables_err.loc[:, DIMENSIONS[dimension]] = dimension_tables_err[DIMENSIONS[dimension]].round(DIGITS_FOR_ERRORS).apply(lambda series: series.apply(format_errors))        
+            dimension_tables_err.loc[:, dimension] = dimension_tables_err[dimension].round(DIGITS_FOR_ERRORS).apply(format_errors)
+            dimension_tables_err = dimension_tables_err[COLUMN_ORDER["all_tables"] + [dimension] + COLUMN_ORDER[value]["dimension_tables"]  + DIMENSIONS[dimension]]
+            dimension_tables_err  = dimension_tables_err.rename(columns={dimension: new_column})
+            all_tables_err[f"{dimension}_err"] = dimension_tables_err
+
+        output[value] = all_tables
+        output[f"{value}_err"] = all_tables_err
+    return output  
+
+
+def get_datasets_tables(all_submission_results: dict, 
+                        all_iqms: dict
+                        ) -> Dict:
+    """
+    creates tables for dataset tab.
+
+    Args:
+        all_submission_results: dict containing all results
+        all_iqms: dict containing all computed results
+    """
+    output = {}
+    result_type = ["normalized","raw"]
+    for value in result_type:
+        all_tables = {}
+        all_tables_err = {}
+        for dataset in DATASETS:
+            dataset_tables = []
+            dataset_tables_err = []
+            for submission in all_submission_results:
+                #get results
+                submission_data = all_iqms[submission][f"{value}_per_dataset"][["backbone", dataset]].copy()
+                submission_data["Model"] = "-"
+                submission_data["# params"] = "-"
+                submission_data["submission"] = submission
+
+                submission_data_err = all_iqms[submission][f"{value}_per_dataset_err"][["backbone", dataset]].copy()
+                submission_data_err["Model"] = "-"
+                submission_data_err["# params"] = "-"
+                submission_data_err["submission"] = submission
+
+                #get parameters
+                parameters = all_submission_results[submission]["results"]
+                new_column = "IQM" if value == "normalized" else "Mean"
+
+                for backbone in all_submission_results[submission]["all_backbones"]:
+                    submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                    submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+                    submission_data = submission_data.rename(columns={dataset: new_column})
+
+                    submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
+                    submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
+                    submission_data_err = submission_data_err.rename(columns={dataset: new_column})
+                    #TODO: add columns
+                dataset_tables.append(submission_data)
+                dataset_tables_err.append(submission_data_err)
+            column = "IQM" if value == "normalized" else "Mean"
+            dataset_tables = pd.concat(dataset_tables)
+            dataset_tables.loc[:, column] = dataset_tables[column].round(DIGITS_FOR_VALUES).apply(format_values)
+            all_tables[dataset] = dataset_tables[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["dataset_tables"]]
+
+            dataset_tables_err = pd.concat(dataset_tables_err)
+            dataset_tables_err.loc[:, column] = dataset_tables_err[column].round(DIGITS_FOR_ERRORS).apply(format_errors)
+            all_tables_err[dataset] = dataset_tables_err[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["dataset_tables"]]
+
+        output[value] = all_tables
+        output[f"{value}_err"] = all_tables_err
+    return output   
+
+
+def get_submission_tables(all_submission_results: dict):
+    output = {}
+    frozen_or_full_ft = ["frozen" ,"full_ft"]
+    config_info = []
+    for method in frozen_or_full_ft:
+        for sub in all_submission_results[method]:
+            config = all_submission_results[method][sub]["config_info"]
+            config = config.to_frame().T
+            config["submission"] = sub
+            config["backbone method"] = method
+            config_info.append(config)
+    output = pd.concat(config_info)
+    output = output[COLUMN_ORDER["submission_info"]]
+    return output   
+
+
+
+
+if __name__ == "__main__":
+    #load results
+    all_submission_results, all_model_names, all_submissions = load_results(folder=RESULTS_DIR)
+
+    #COMBINED NORM
+    norm_base_results= [] 
+    for method in NORM_BASE_SUBMISSION:
+        for sub in NORM_BASE_SUBMISSION[method]:
+            norm_base_results.append(all_submission_results[method][sub]["results"].copy())
+    norm_base_results = pd.concat(norm_base_results)
+    benchmark_name = "leaderboard_combined"  
+    compute_tools.make_normalizer(norm_base_results.reset_index(), 
+                                    metrics=("test metric",), 
+                                    benchmark_name=benchmark_name)
+
+    overall_performance_tables = {}
+    performance_by_dimension_tables = {}
+    datasets_tables = {}
+    for method in ["full_ft","frozen"]:
+        method_iqms = compute_all_iqms(
+                                all_submission_results =  all_submission_results[method],
+                                benchmark_name = benchmark_name,
+                                )
+    
+        #create tables to be rendered
+        overall_performance_tables[method] = get_overall_performance_table(all_submission_results=all_submission_results[method],
+                                                                    all_iqms=method_iqms)
+        performance_by_dimension_tables[method] = get_performance_by_dimension_table(all_submission_results=all_submission_results[method],
+                                                                            all_iqms=method_iqms)
+        datasets_tables[method] = get_datasets_tables(all_submission_results=all_submission_results[method],
+                                                all_iqms=method_iqms)
+
+    submission_info_table = get_submission_tables(all_submission_results=all_submission_results)
+        
+    compiled_results = {
+                        "overall_performance_tables": overall_performance_tables,
+                        "performance_by_dimension_tables": performance_by_dimension_tables,
+                        "datasets_tables": datasets_tables,
+                        "submission_info_table": submission_info_table
+                        }
+
+    with open(f'{RESULTS_DIR}/compiled.pkl', 'wb') as handle:
+        pickle.dump(compiled_results, handle, protocol=pickle.HIGHEST_PROTOCOL)

utils/compute_tools.py

@@ -0,0 +1,154 @@
+# Copyright contributors to the geobench project
+# modified from geobench (https://github.com/ServiceNow/geo-bench/blob/main/geobench/plot_tools.py)
+
+import os
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from utils.constants import NORMALIZER_DIR
+import json
+from scipy.stats import trim_mean, sem
+from scipy.stats.mstats import trim
+
+np.random.seed(100)
+
+def biqm(scores):
+    """Return a bootstram sample of iqm."""
+    b_scores = np.random.choice(scores, size=len(scores), replace=True)
+    return trim_mean(b_scores, proportiontocut=0.25, axis=None)
+
+
+def trimmed_sem(scores):
+    """Interquantile mean."""
+    scores = trim(scores, limits=(0.25,0.25), relative=True)
+    scores = scores.data[np.where(~scores.mask)] 
+    return sem(scores)
+
+
+def iqm(scores):
+    """Interquantile mean."""
+    return trim_mean(scores, proportiontocut=0.25, axis=None)
+
+
+def bootstrap_iqm(
+    df, group_keys=("model", "dataset"), metric="test_metric", repeat=100
+):
+    """Boostram of seeds for all model and all datasets to comput iqm score distribution."""
+    df_list = []
+    for i in range(repeat):
+        series = df.groupby(list(group_keys))[metric].apply(biqm)
+        df_list.append(series.to_frame().reset_index())
+
+    return pd.concat(df_list)
+
+
+def bootstrap_iqm_aggregate(df, metric="test_metric", repeat=100):
+    """Stratified bootstrap (by dataset) of all seeds to compute iqm score distribution for each backbone."""
+    group = df.groupby(["backbone", "dataset"])
+
+    df_list = []
+    for i in range(repeat):
+        new_df = group.sample(frac=1, replace=True, random_state=100+i)
+        series = new_df.groupby(["backbone"])[metric].apply(iqm)
+        df_list.append(series.to_frame().reset_index())
+
+    new_df = pd.concat(df_list)
+    new_df.loc[:, "dataset"] = "aggregated"
+    return new_df
+
+
+def bootstrap_mean_aggregate(df, metric="test_metric", repeat=100):
+    """Stratified bootstrap (by dataset) of all seeds to compute mean score distribution for each backbone."""
+    group = df.groupby(["backbone", "dataset"])
+
+    df_list = []
+    for i in range(repeat):
+        new_df = group.sample(frac=1, replace=True, random_state=100+i)
+        series = new_df.groupby(["backbone"])[metric].apply(np.mean)
+        df_list.append(series.to_frame().reset_index())
+
+    new_df = pd.concat(df_list)
+    new_df.loc[:, "dataset"] = "aggregated"
+    return new_df
+
+
+
+def average_seeds(df, group_keys=("model", "dataset"), metric="test metric"):
+    """Average seeds for all model and all datasets."""
+    df_avg = df.groupby(list(group_keys))[metric].mean()
+    df_avg = df_avg.unstack(level="dataset")
+
+    df_avg = df_avg.round(3)
+    return df_avg
+
+
+def extract_1x_data(df_all):
+    """Extract only resutls trained on 100% of the data"""
+    return df_all[
+        (df_all["partition name"] == "1.00x train") | (df_all["partition name"] == "default")
+    ].copy()
+
+
+class Normalizer:
+    """Class used to normalize results beween min and max for each dataset."""
+
+    def __init__(self, range_dict):
+        """Initialize a new instance of Normalizer class."""
+        self.range_dict = range_dict
+
+    def __call__(self, ds_name, values, scale_only=False):
+        """Call the Normalizer class."""
+        mn, mx = self.range_dict[ds_name]
+        range = mx - mn
+        if scale_only:
+            return values / range
+        else:
+            return (values - mn) / range
+
+    def from_row(self, row, scale_only=False):
+        """Normalize from row."""
+        return [self(ds_name, val, scale_only=scale_only) for ds_name, val in row.items()]
+
+    def normalize_data_frame(self, df, metric):
+        """Normalize the entire dataframe."""
+        new_metric = f"normalized {metric}"
+        df[new_metric] = df.apply(lambda row: self.__call__(row["dataset"], row[metric]), axis=1)
+        return new_metric
+
+    def save(self, benchmark_name):
+        """Save normalizer to json file."""
+
+        if not os.path.exists(f"{NORMALIZER_DIR}/{benchmark_name}/"):
+            print("making directory")
+            os.makedirs(f"{NORMALIZER_DIR}/{benchmark_name}/")
+        with open(f"{NORMALIZER_DIR}/{benchmark_name}/normalizer.json", "w") as f:
+            json.dump(self.range_dict, f, indent=2)
+
+
+def load_normalizer(benchmark_name):
+    """Load normalizer from json file."""
+    with open(f"{NORMALIZER_DIR}/{benchmark_name}/normalizer.json", "r") as f:
+        range_dict = json.load(f)
+    return Normalizer(range_dict)
+
+
+def make_normalizer(data_frame, metrics=("test metric",), benchmark_name="leaderboard_combined"):
+    """Extract min and max from data_frame to build Normalizer object for all datasets."""
+    datasets = data_frame["dataset"].unique()
+    range_dict = {}
+
+    for dataset in datasets:
+        sub_df = data_frame[data_frame["dataset"] == dataset]
+        data = []
+        for metric in metrics:
+            data.append(sub_df[metric].to_numpy())
+        range_dict[dataset] = (np.min(data), np.max(data))
+
+    normalizer = Normalizer(range_dict)
+
+    if benchmark_name:
+        normalizer.save(benchmark_name)
+
+    return normalizer
+
+

utils/constants.py

@@ -0,0 +1,129 @@
+from pathlib import Path
+
+NORM_BASE_SUBMISSION = {
+        "full_ft": ["114be1f0-5a41-43a5-b4e6-7fb683bc01ec", "2ce4a907-7ae3-45d3-a07a-558f8d0d758b"],# "b5e5e59d-044b-4848-8323-aceb8c535ed9"],
+        # "frozen": ["80100fc6-dc1a-4514-b946-341157eaf816"],
+}
+DIGITS_FOR_VALUES = 3
+DIGITS_FOR_ERRORS = 6
+REQUIRED_SEEDS = 5
+
+
+DIMENSIONS = {
+            "Multi-Spectral-Dependent": ["benv2", "biomassters", "pastis", "so2sat", "cloudsen12", "spacenet2", "burn_scars", "fotw",],#
+            "Multi-Temporal": ['kuro_siwo','pastis', 'biomassters', 'dynamic_earthnet', ],
+            "Pixel-wise": ['kuro_siwo', 'pastis', 'burn_scars', 'spacenet2', 'cloudsen12',  'caffe', 'flair2','dynamic_earthnet','biomassters',  "spacenet7", "fotw",],
+            "Classification": ['so2sat', 'forestnet', 'benv2', 'treesatai'],
+            "Detection (Object/Instance)": ["substation", "everwatch", "nzcattle", "pastis_r",],
+            "Under 10m Resolution": ["spacenet2","treesatai", "flair2",'dynamic_earthnet',  "spacenet7"],
+            "10m and Above Resolution": ["biomassters", "so2sat", "kuro_siwo", "cloudsen12", "pastis", "benv2", "forestnet", "burn_scars", "caffe", "fotw",],#
+            "RGB/NIR": ["flair2",  "treesatai", 'dynamic_earthnet', "spacenet7", "fotw",], #
+            "Core": ['kuro_siwo', 'pastis', 'burn_scars', 'cloudsen12',  'flair2', "spacenet7", 'benv2', 'treesatai', 'biomassters', "fotw","substation", "everwatch", ],
+            }
+
+
+DIMENSION_INFO = {
+            "Multi-Spectral-Dependent": "datasets that have a statistically significant increase in perfromance when mutlispectral bands are used",
+            "Multi-Temporal": "datasets with more than 1 timestamps used as an input",
+            "Pixel-wise": "datasets for pixel-wise segmentation and regression",
+            "Classification": "single-label and multi-label classification datasets",
+            "Detection (Object/Instance)":"datasets for instance segmentation and object detection",
+            "Under 10m Resolution": "datasets with resolution <= 1 metre",
+            "10m and Above Resolution": "datasets with 10 metres =< resolution <= 30 metres",
+            "RGB/NIR": "datasets using  Red, Green, Blue, and NIR bands",
+            "Core": "subset with datasets from each dimension",
+            }
+
+
+DATASETS = [
+            'biomassters', 'so2sat', 'forestnet', 'benv2', 'treesatai',
+            'kuro_siwo', 'dynamic_earthnet', 'pastis', 'burn_scars', 'spacenet2',
+            'cloudsen12', 'fotw', 'caffe', 'flair2',  "spacenet7",
+            "substation", "everwatch", "nzcattle", "pastis_r",
+            ]
+
+
+
+DATASET_INFO = {
+                "Dataset": [item.replace("_", " ").title() for item in DATASETS],
+                "Description": [
+                    "regression dataset for Above Ground Biomass (AGB) prediction", #biomassters
+                    "multi-class classifictaion dataset for Global Local Climate Zones", #so2sat
+                    "multi-class classifictaion dataset for deforestation drivers", #forestnet
+                    "multi-label classifictaion dataset for land cover",#benv2
+                    "multi-label classifictaion dataset for tree species",#treesatai
+                    "SAR semantic segmentation dataset for rapid flood mapping",#kuro_siwo_mean
+                    "semantic segmentation dataset for land use/land cover",#dynamic_earthnet
+                    "semantic segmentation dataset for agricultural parcels",#pastis
+                    "semantic segmentation dataset for burn scars",#burn_scars
+                    "semantic segmentation dataset for building detection",#spacenet2
+                    "semantic segmentation dataset for cloud and cloud shadow detection",#cloudsen12
+                    "semantic/instance segmentation dataset for agricultural fields ",#fotw
+                    "semantic/instance segmentation dataset for glacier calving front extraction",#caffe
+                    "semantic segmentation dataset for land use/land cover",#flair2
+                    "semantic segmentation dataset for building detection",#spacenet7
+                    "instance segmentation dataset for substations",#substation
+                    "object detection dataset for bird species",#everwatch
+                    "object detection dataset for cattle",#nzcattle
+                    "instance segmentation dataset for crop type mapping",#pastis_r
+                    ],
+                "Dimensions": [", ".join([dim for dim, data_list in DIMENSIONS.items() if dataset in data_list]) for dataset in DATASETS]
+                }
+
+
+COLUMN_ORDER = {
+                "raw": {
+                    "dataset_tables": ['Mean'],
+                    "dimension_tables": []
+                },
+                "normalized": {
+                    "overall_table": [
+                        "Core",  "Multi-Spectral-Dependent",  "Multi-Temporal", "Pixel-wise", "Classification",  "Detection (Object/Instance)", 
+                        "Under 10m Resolution", "10m and Above Resolution",
+                        "RGB/NIR",
+                        ], 
+                    "dataset_tables": ['IQM'] ,
+                    "dimension_tables": []
+
+                },
+                "all_tables": ['Model', '# params', 'submission'],
+                "submission_info": ["submission", "backbone method", "decoder", "n_trials", "early_stop_patience", "data_percentages", "batch_size_selection" ],
+                }
+
+root = Path(__file__).parent.resolve()
+root = "/".join(str(root).split("/")[:-1])
+RESULTS_DIR = f"{root}/results"
+MODEL_INFO_FILE = f"{root}/utils/model_info.json"
+NORMALIZER_DIR = f"{root}/utils/normalizer"
+
+
+#for validation of new submissions
+NEW_SUBMISSION_FOLDER = f"{root}/new_submission"
+CSV_FILE = "results_and_parameters.csv"
+JSON_FILE = "additional_info.json"
+NEW_SUBMISSION_COLUMN_INFO = {
+                                "string_cols": ['dataset', 'Metric', 'experiment_name', 'partition name', 'backbone', 'decoder','batch_size_selection', 'frozen_or_full_ft'],
+                                "integer_cols": ['early_stop_patience', 'n_trials', 'Seed', 'data_percentages', 'batch_size'],
+                                "float_cols": ['weight_decay', 'lr', 'test metric', ]
+                            }
+NEW_SUBMISSION_COLUMN_NAMES = []
+for key, value in NEW_SUBMISSION_COLUMN_INFO.items():
+    NEW_SUBMISSION_COLUMN_NAMES.extend(value)
+
+                                
+JSON_FORMAT = {
+                "Paper Link": "N/A",
+                "Code Repository Link ": "N/A",
+                "License": "N/A",
+                "Number of HPO trials": "16",
+                "Additional information about submission": "N/A",
+                "Comments on new models in submission": "N/A",
+                "New model info": 
+                    [
+                        {
+                            "model_display_name": "TBD",
+                            "model_size": "TBD", 
+                            "unique_backbone_key": "TBD"
+                        }
+                    ]
+                }   

utils/input_validation.py

@@ -0,0 +1,218 @@
+import pandas as pd
+import json
+import os
+import numpy as np
+import uuid
+from utils.constants import (NEW_SUBMISSION_FOLDER, CSV_FILE, JSON_FILE, DIMENSIONS, 
+                        NEW_SUBMISSION_COLUMN_INFO, NEW_SUBMISSION_COLUMN_NAMES,
+                        JSON_FORMAT, MODEL_INFO_FILE, RESULTS_DIR, REQUIRED_SEEDS)
+
+
+def check_correct_file_type(folder_contents) -> bool:
+    """ 
+        checks that folder has 2 items: a csv file and a json file
+    """
+    contains_correct_files = (len(folder_contents) == 2) and (CSV_FILE in folder_contents) and (JSON_FILE in folder_contents)
+    if not contains_correct_files:
+        print("\nInput Validation Error: Please check that the {NEW_SUBMISSION_FOLDER} contains the files: \
+              {CSV_FILE} and {JSON_FILE}")
+        return False
+    return True
+
+
+def check_csv_columns_datatypes() -> bool:
+    """ 
+        checks that csv file has only required columns and columns have correct data types
+    """
+    #check for correct columns
+    csv_data = pd.read_csv(f"{NEW_SUBMISSION_FOLDER}/{CSV_FILE}")
+    submitted_csv_column_names = set(csv_data.columns)
+    expected_column_names = set(NEW_SUBMISSION_COLUMN_NAMES)
+    for item in expected_column_names:
+        if item not in submitted_csv_column_names:
+            print(f"The following column is missing: {item}")
+    correct_columns = expected_column_names.issubset(submitted_csv_column_names)
+    if not correct_columns:
+        print(f"\nInput Validation Error: Please ensure that the csv file contains the following columns: {NEW_SUBMISSION_COLUMN_NAMES}")
+    
+    #check for correct dtype
+    correct_dtypes = []
+    for col in NEW_SUBMISSION_COLUMN_INFO["string_cols"]:
+        if col in csv_data.columns:
+            print(f"{col} is string/object:{pd.api.types.is_object_dtype(csv_data[col])}")
+            correct_dtypes.append(pd.api.types.is_object_dtype(csv_data[col]))
+    for col in NEW_SUBMISSION_COLUMN_INFO["integer_cols"]:
+        if col in csv_data.columns:
+            print(f"{col} is numeric: {pd.api.types.is_numeric_dtype(csv_data[col])}")
+            correct_dtypes.append(pd.api.types.is_numeric_dtype(csv_data[col]))
+    for col in NEW_SUBMISSION_COLUMN_INFO["float_cols"]:
+        if col in csv_data.columns:
+            print(f"{col} is numeric: {pd.api.types.is_numeric_dtype(csv_data[col])}")
+            correct_dtypes.append(pd.api.types.is_numeric_dtype(csv_data[col]))
+    correct_dtypes = all(correct_dtypes)
+    if not correct_dtypes:
+        print(f"\nInput Validation Error: Please ensure that the csv columns have the correct datatypes as follows: \n\
+                string/object type columns: {NEW_SUBMISSION_COLUMN_INFO['string_cols']} \n\
+                numeric/integer type columns: {NEW_SUBMISSION_COLUMN_INFO['integer_cols']}\
+                                                {NEW_SUBMISSION_COLUMN_INFO['float_cols']}")
+    return correct_columns, correct_dtypes 
+
+
+def check_correct_entries_per_dataset(required_seeds: int = REQUIRED_SEEDS) -> bool:
+    """
+        checks for correct number of runs per backbone/dataset combination
+        checks for required number of unique seeds
+    """
+    csv_data = pd.read_csv(f"{NEW_SUBMISSION_FOLDER}/{CSV_FILE}")
+    count_values = csv_data.groupby(["backbone", "dataset"]).count()
+    count_values = list(set(count_values["test metric"].tolist()))
+    correct_num_values = (len(count_values) == 1) and (count_values[0] == required_seeds)
+    if not correct_num_values:
+        print(f"\nInput Validation Error: Please ensure that each backbone/dataset combination has {required_seeds} entries")
+
+    count_seeds = csv_data.groupby(["backbone", "dataset"]).nunique()
+    count_seeds = list(set(count_seeds["Seed"].tolist()))
+    correct_num_seeds = (len(count_seeds) == 1) and (count_seeds[0] == required_seeds)
+    if not correct_num_seeds:
+        print(f"\nInput Validation Warning: Please ensure that each backbone/dataset combination has {required_seeds} unique seeds")
+    return correct_num_values, correct_num_seeds
+
+
+def check_json_keys() -> bool:
+    """ 
+        checks json file has required keys and subkeys, 
+        check json file values have correct data type
+    """
+    with open(f"{NEW_SUBMISSION_FOLDER}/{JSON_FILE}") as f:
+        json_submission_data = json.load(f)
+    #TBD: check json file nested values have correct data type
+    all_required_keys = []
+    for key, value in JSON_FORMAT.items():
+        if (key in json_submission_data) and (type(value) == type(json_submission_data[key])): 
+            all_required_keys.append(True)
+        else:
+            all_required_keys.append(False)
+    all_required_keys = all(all_required_keys)
+    if not all_required_keys:
+        print(f"\nInput Validation Error: Please ensure that json file has the correct keys and datatypes")
+    
+    return all_required_keys
+
+
+def check_has_atleast_one_dimension() -> bool:
+    """
+        check that submission contains datasets required for at least one submission
+    """
+    csv_data = pd.read_csv(f"{NEW_SUBMISSION_FOLDER}/{CSV_FILE}")
+    submitted_csv_datasets = set(csv_data["dataset"].tolist())
+    contains_atleast_one_dimension = []
+    for dimension, datasets in DIMENSIONS.items():
+        datasets = set(datasets)
+        contains_atleast_one_dimension.append(datasets.issubset(submitted_csv_datasets))
+    contains_atleast_one_dimension = any(contains_atleast_one_dimension)
+    if not contains_atleast_one_dimension:
+        print("\nInput Validation Error: Please check that the submission contains all datasets for one or more dimensions")
+        print(f'currently submitted datasets are: {submitted_csv_datasets}')
+    return contains_atleast_one_dimension
+
+
+def check_has_frozen_or_full_ft() -> bool:
+    """
+        check that submission has correct values in frozen_or_full_ft column
+    """
+    csv_data = pd.read_csv(f"{NEW_SUBMISSION_FOLDER}/{CSV_FILE}")
+    frozen_or_full_ft = set(csv_data["frozen_or_full_ft"].tolist())
+    correct_values = True
+    for item in frozen_or_full_ft:
+        if not ((item == "frozen") or (item == "full_ft")):
+            correct_values = False
+    if not correct_values:
+        print("\nInput Validation Error: Please check that the frozen_or_full_ft column contains only 'frozen' or 'full_ft'")
+        print(f'currently submitted values are: {frozen_or_full_ft}')
+    return correct_values
+
+def update_new_backbones_and_models():
+    """
+        checks if backbone exists in model_info.json (used to display results)
+        if not, information on the new model is added to the json file
+    """
+    with open(f"{NEW_SUBMISSION_FOLDER}/{JSON_FILE}") as f:
+        json_submission_data = json.load(f)
+
+    #read model info
+    with open(MODEL_INFO_FILE) as f:
+        existing_model_info = json.load(f)
+    for item in json_submission_data["New model info"]:
+        submitted_backbone = item["unique_backbone_key"]
+        if submitted_backbone not in existing_model_info["BACKBONE_NAMES"]:
+            existing_model_info["BACKBONE_NAMES"][submitted_backbone] = item["model_display_name"]
+            existing_model_info["MODEL_SIZE"][submitted_backbone] = item["model_size"]
+    
+    #save new information
+    with open(MODEL_INFO_FILE, 'w') as fp:
+        json.dump(existing_model_info, fp)
+
+
+def validate_new_submission() -> bool:
+    """
+    
+    """
+    #get folder contents
+    if not os.path.exists(NEW_SUBMISSION_FOLDER): return
+    folder_contents = os.listdir(NEW_SUBMISSION_FOLDER)
+    items_to_ignore = ['.DS_Store']
+    for item in items_to_ignore:
+        if item in folder_contents: folder_contents.remove(item)
+    if len(folder_contents) == 0:
+        print("no new submissions")
+        return
+    
+    #check all conditions
+    correct_file_type = check_correct_file_type(folder_contents)
+    correct_columns, correct_dtypes = check_csv_columns_datatypes()
+    correct_num_values, correct_num_seeds = check_correct_entries_per_dataset()
+    correct_json_keys = check_json_keys()
+    contains_atleast_one_dimension = check_has_atleast_one_dimension()
+    correct_frozen_or_full_ft = check_has_frozen_or_full_ft()
+    all_checks_passed = all([correct_file_type, correct_columns, correct_dtypes,
+                            correct_json_keys, correct_num_values, #correct_num_seeds,
+                            contains_atleast_one_dimension, correct_frozen_or_full_ft])
+    
+    if all_checks_passed:
+        submission_id = uuid.uuid4()
+        os.makedirs(f"{RESULTS_DIR}/{submission_id}")
+
+        #copy only required keys in json file to new submission folder
+        with open(f"{NEW_SUBMISSION_FOLDER}/{JSON_FILE}") as f:
+            json_submission_data = json.load(f)
+        new_dict = {}
+        for key, value in JSON_FORMAT.items():
+            if value == "TBD": continue
+            new_dict[key] = json_submission_data[key]
+        with open(f"{RESULTS_DIR}/{submission_id}/{JSON_FILE}", 'w') as fp:
+            json.dump(new_dict, fp)
+
+        #copy only required columns in csv file to new submission folder
+        csv_data = pd.read_csv(f"{NEW_SUBMISSION_FOLDER}/{CSV_FILE}")
+        csv_data = csv_data[NEW_SUBMISSION_COLUMN_NAMES]
+        csv_data.to_csv(f"{RESULTS_DIR}/{submission_id}/{CSV_FILE}", index=False)
+
+        #add any new model info to model_info.json
+        update_new_backbones_and_models()
+
+        #reset NEW_SUBMISSION_FOLDER
+        os.system(f"rm -r {NEW_SUBMISSION_FOLDER}/")
+        os.makedirs(NEW_SUBMISSION_FOLDER)
+        return
+    else:
+        print("\nThe new sumbission has not been formatted correctly. Please fix the errors above")
+        raise ValueError
+
+    
+
+
+
+
+    
+if __name__ == "__main__":
+    validate_new_submission()

utils/model_info.json

@@ -0,0 +1,52 @@
+{"BACKBONE_NAMES": {
+    "vit_large_patch16": "ScaleMAE-ViT 300M", 
+    "resnet101": "Resnet101", 
+    "prithvi_vit_100": "Prithvi-EO-V1-100", 
+    "prithvi_eo_v2_100": "Prithvi-EO-V2-100", 
+    "resnet50": "Resnet50-ImageNet", 
+    "dofa_base_patch16_224": "DOFA-ViT-B", 
+    "dofa_large_patch16_224": "DOFA-ViT 300M", 
+    "clay_v1_base": "Clay-V1 ViT-B", 
+    "prithvi_eo_v2_300": "Prithvi-EO-V2 300M", 
+    "prithvi_eo_v2_300_tl": "Prithvi-EO-V2 300M TL", 
+    "prithvi_eo_v2_600": "Prithvi-EO-V2 600M", 
+    "prithvi_eo_v2_600_tl": "Prithvi-EO-V2 600M TL", 
+    "satlas_swin_b_sentinel2_si_ms": "Satlas-Swin 100M", 
+    "ssl4eos12_resnet50_sentinel2_all_dino": "Resnet50-DINO", 
+    "ssl4eos12_resnet50_sentinel2_all_moco": "Resnet50-MOCO", 
+    "ssl4eos12_resnet50_sentinel2_all_decur": "Resnet50-DeCUR", 
+    "satlas_resnet50_sentinel2_si_ms_satlas": "Satlas-Resnet50", 
+    "terramind_v1_base": "TerraMind-V1-Base", 
+    "terramind_v1_large": "TerraMind-V1-Large", 
+    "convnext_xlarge_fb_in22k": "ConvNext-XLarge-ImageNet", 
+    "convnext_large_fb_in22k": "ConvNext-Large-ImageNet", 
+    "dinov3_vitl16": "DinoV3-ViT-L-SAT", 
+    "dinov3_convnext_large": "DinoV3-ConvNext-Large-WEB", 
+    "satlas_swin_b_naip_si_rgb": "Satlas-SwinB-Naip", 
+    "TBD": "TBD", "": ""}, 
+"MODEL_SIZE": {
+    "vit_large_patch16": "300M", 
+    "resnet101": "42M", 
+    "prithvi_vit_100": "100M", 
+    "prithvi_eo_v2_100": "100M", 
+    "resnet50": "25M", 
+    "dofa_base_patch16_224": "100M", 
+    "dofa_large_patch16_224": "300M", 
+    "clay_v1_base": "100M", 
+    "prithvi_eo_v2_300": "300M", 
+    "prithvi_eo_v2_300_tl": "300M", 
+    "prithvi_eo_v2_600": "600M", 
+    "prithvi_eo_v2_600_tl": "600M", 
+    "satlas_swin_b_sentinel2_si_ms": "100M", 
+    "ssl4eos12_resnet50_sentinel2_all_dino": "25M", 
+    "ssl4eos12_resnet50_sentinel2_all_moco": "25M", 
+    "ssl4eos12_resnet50_sentinel2_all_decur": "25M", 
+    "satlas_resnet50_sentinel2_si_ms_satlas": "25M", 
+    "terramind_v1_base": "100M", 
+    "terramind_v1_large": "300M", 
+    "convnext_xlarge_fb_in22k": "390M", 
+    "convnext_large_fb_in22k": "230M", 
+    "satlas_swin_b_naip_si_rgb": "100M", 
+    "dinov3_vitl16": "300M", 
+    "dinov3_convnext_large": "230M", 
+    "TBD": "TBD", "": ""}}

utils/normalizer/leaderboard_combined/normalizer.json

@@ -0,0 +1,78 @@
+{
+  "treesatai": [
+    0.5388144254684448,
+    0.6719674468040466
+  ],
+  "cloudsen12": [
+    0.5892206430435181,
+    0.7069313526153564
+  ],
+  "dynamic_earthnet": [
+    0.1660756915807724,
+    0.3561779260635376
+  ],
+  "forestnet": [
+    0.4642497599124908,
+    0.6082578301429749
+  ],
+  "spacenet2": [
+    0.8431392908096313,
+    0.8844738006591797
+  ],
+  "fotw": [
+    0.5552823543548584,
+    0.6179081797599792
+  ],
+  "caffe": [
+    0.5596897006034851,
+    0.7002602815628052
+  ],
+  "burn_scars": [
+    0.6498024463653564,
+    0.9017086625099182
+  ],
+  "pastis": [
+    0.2038679122924804,
+    0.43324875831604
+  ],
+  "benv2": [
+    0.7138733267784119,
+    0.7664718627929688
+  ],
+  "kuro_siwo": [
+    0.3207171559333801,
+    0.7027350068092346
+  ],
+  "spacenet7": [
+    0.3912872672080993,
+    0.6402554512023926
+  ],
+  "flair2": [
+    0.4062314331531524,
+    0.5503402948379517
+  ],
+  "biomassters": [
+    0.9482341889789176,
+    0.9600944613389646
+  ],
+  "so2sat": [
+    0.5111562013626099,
+    0.6541582345962524
+  ],
+  "everwatch": [
+    0.2177008986473083,
+    0.3155497610569
+  ],
+  "nzcattle": [
+    0.2819505035877228,
+    0.401744931936264
+  ],
+  "pastis_r": [
+    0.084835797548294,
+    0.1661437004804611
+  ],
+  "substation": [
+    0.1244395673274993,
+    0.2031933367252349
+  ]
+}