vit-custom-classifier

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the lus dataset. It achieves the following results on the evaluation set:

• Loss: 0.2133
• Accuracy: 0.9149

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3746	0.9615	100	0.3226	0.8994
0.1843	1.9231	200	0.5139	0.8658
0.0866	2.8846	300	0.3348	0.9078
0.0618	3.8462	400	0.2905	0.9203
0.0134	4.8077	500	0.3054	0.9203

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 2.14.4
• Tokenizers 0.21.1