mahjong_soul_vision

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

• Loss: 0.0535
• Accuracy: 0.9955
• F1: 0.9955
• Recall: 0.9955

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall
3.525	1.0	17	3.5241	0.0390	0.0191	0.0390
3.4705	2.0	34	3.4880	0.1059	0.0696	0.1059
3.3849	3.0	51	3.4336	0.1516	0.1033	0.1516
3.2368	4.0	68	3.3594	0.2129	0.1628	0.2129
3.1311	5.0	85	3.2684	0.2843	0.2199	0.2843
2.9889	6.0	102	3.1530	0.3701	0.3219	0.3701
2.7421	7.0	119	3.0133	0.4849	0.4358	0.4849
2.5617	8.0	136	2.8537	0.6120	0.5663	0.6120
2.318	9.0	153	2.6894	0.7458	0.7044	0.7458
2.0861	10.0	170	2.5204	0.8038	0.7655	0.8038
1.8374	11.0	187	2.3533	0.8261	0.7970	0.8261
1.6962	12.0	204	2.1952	0.8662	0.8492	0.8662
1.4737	13.0	221	2.0266	0.9108	0.9018	0.9108
1.3726	14.0	238	1.8545	0.9599	0.9592	0.9599
1.1261	15.0	255	1.7290	0.9732	0.9725	0.9732
1.0357	16.0	272	1.5866	0.9777	0.9776	0.9777
0.9068	17.0	289	1.4664	0.9810	0.9812	0.9810
0.8355	18.0	306	1.3424	0.9833	0.9828	0.9833
0.7568	19.0	323	1.2254	0.9866	0.9865	0.9866
0.6386	20.0	340	1.1300	0.9877	0.9876	0.9877
0.5827	21.0	357	1.0365	0.9911	0.9911	0.9911
0.5086	22.0	374	0.9475	0.9933	0.9933	0.9933
0.4486	23.0	391	0.8839	0.9911	0.9911	0.9911
0.4316	24.0	408	0.8186	0.9922	0.9922	0.9922
0.406	25.0	425	0.7656	0.9922	0.9922	0.9922
0.3478	26.0	442	0.7115	0.9933	0.9933	0.9933
0.3394	27.0	459	0.6696	0.9933	0.9933	0.9933
0.2961	28.0	476	0.6306	0.9933	0.9933	0.9933
0.2858	29.0	493	0.5913	0.9933	0.9933	0.9933
0.2614	30.0	510	0.5601	0.9933	0.9933	0.9933
0.2485	31.0	527	0.5323	0.9944	0.9944	0.9944
0.2205	32.0	544	0.5103	0.9933	0.9933	0.9933
0.2092	33.0	561	0.4861	0.9922	0.9921	0.9922
0.2047	34.0	578	0.4625	0.9944	0.9944	0.9944
0.1825	35.0	595	0.4436	0.9933	0.9933	0.9933
0.1743	36.0	612	0.4237	0.9944	0.9944	0.9944
0.1685	37.0	629	0.4026	0.9944	0.9944	0.9944
0.1589	38.0	646	0.3875	0.9944	0.9944	0.9944
0.1484	39.0	663	0.3739	0.9944	0.9944	0.9944
0.143	40.0	680	0.3613	0.9944	0.9944	0.9944
0.1423	41.0	697	0.3494	0.9944	0.9944	0.9944
0.1299	42.0	714	0.3372	0.9922	0.9921	0.9922
0.1196	43.0	731	0.3251	0.9944	0.9944	0.9944
0.1195	44.0	748	0.3165	0.9933	0.9933	0.9933
0.1163	45.0	765	0.3042	0.9944	0.9944	0.9944
0.1059	46.0	782	0.2940	0.9944	0.9944	0.9944
0.105	47.0	799	0.2857	0.9944	0.9944	0.9944
0.1011	48.0	816	0.2773	0.9944	0.9944	0.9944
0.0952	49.0	833	0.2685	0.9944	0.9944	0.9944
0.0894	50.0	850	0.2626	0.9944	0.9944	0.9944
0.0889	51.0	867	0.2539	0.9944	0.9944	0.9944
0.0875	52.0	884	0.2469	0.9955	0.9955	0.9955
0.0856	53.0	901	0.2417	0.9944	0.9944	0.9944
0.0838	54.0	918	0.2344	0.9944	0.9944	0.9944
0.0783	55.0	935	0.2290	0.9955	0.9955	0.9955
0.0751	56.0	952	0.2225	0.9955	0.9955	0.9955
0.0697	57.0	969	0.2165	0.9955	0.9955	0.9955
0.073	58.0	986	0.2132	0.9955	0.9955	0.9955
0.0693	59.0	1003	0.2069	0.9955	0.9955	0.9955
0.0626	60.0	1020	0.2042	0.9944	0.9944	0.9944
0.0622	61.0	1037	0.1986	0.9955	0.9955	0.9955
0.0607	62.0	1054	0.1959	0.9955	0.9955	0.9955
0.0593	63.0	1071	0.1898	0.9955	0.9955	0.9955
0.0588	64.0	1088	0.1868	0.9955	0.9955	0.9955
0.0559	65.0	1105	0.1828	0.9955	0.9955	0.9955
0.0562	66.0	1122	0.1792	0.9955	0.9955	0.9955
0.0528	67.0	1139	0.1756	0.9955	0.9955	0.9955
0.0535	68.0	1156	0.1717	0.9955	0.9955	0.9955
0.0508	69.0	1173	0.1690	0.9955	0.9955	0.9955
0.0474	70.0	1190	0.1659	0.9955	0.9955	0.9955
0.0475	71.0	1207	0.1622	0.9955	0.9955	0.9955
0.0469	72.0	1224	0.1594	0.9955	0.9955	0.9955
0.0464	73.0	1241	0.1569	0.9955	0.9955	0.9955
0.0449	74.0	1258	0.1548	0.9955	0.9955	0.9955
0.042	75.0	1275	0.1517	0.9955	0.9955	0.9955
0.0429	76.0	1292	0.1498	0.9955	0.9955	0.9955
0.0384	77.0	1309	0.1461	0.9955	0.9955	0.9955
0.038	78.0	1326	0.1447	0.9955	0.9955	0.9955
0.0388	79.0	1343	0.1421	0.9955	0.9955	0.9955
0.0419	80.0	1360	0.1404	0.9955	0.9955	0.9955
0.0396	81.0	1377	0.1381	0.9955	0.9955	0.9955
0.0358	82.0	1394	0.1360	0.9955	0.9955	0.9955
0.0379	83.0	1411	0.1340	0.9955	0.9955	0.9955
0.0359	84.0	1428	0.1316	0.9955	0.9955	0.9955
0.0341	85.0	1445	0.1301	0.9955	0.9955	0.9955
0.0336	86.0	1462	0.1281	0.9955	0.9955	0.9955
0.0318	87.0	1479	0.1264	0.9955	0.9955	0.9955
0.0332	88.0	1496	0.1243	0.9955	0.9955	0.9955
0.0337	89.0	1513	0.1230	0.9955	0.9955	0.9955
0.0315	90.0	1530	0.1213	0.9955	0.9955	0.9955
0.0307	91.0	1547	0.1197	0.9955	0.9955	0.9955
0.0308	92.0	1564	0.1181	0.9955	0.9955	0.9955
0.0291	93.0	1581	0.1168	0.9955	0.9955	0.9955
0.0284	94.0	1598	0.1154	0.9955	0.9955	0.9955
0.0268	95.0	1615	0.1138	0.9955	0.9955	0.9955
0.029	96.0	1632	0.1128	0.9955	0.9955	0.9955
0.0272	97.0	1649	0.1110	0.9955	0.9955	0.9955
0.0254	98.0	1666	0.1095	0.9955	0.9955	0.9955
0.0263	99.0	1683	0.1085	0.9955	0.9955	0.9955
0.0256	100.0	1700	0.1070	0.9955	0.9955	0.9955
0.0247	101.0	1717	0.1060	0.9955	0.9955	0.9955
0.0245	102.0	1734	0.1053	0.9955	0.9955	0.9955
0.0244	103.0	1751	0.1038	0.9955	0.9955	0.9955
0.0245	104.0	1768	0.1022	0.9955	0.9955	0.9955
0.024	105.0	1785	0.1016	0.9955	0.9955	0.9955
0.0231	106.0	1802	0.1009	0.9955	0.9955	0.9955
0.0227	107.0	1819	0.0994	0.9955	0.9955	0.9955
0.0227	108.0	1836	0.0987	0.9955	0.9955	0.9955
0.0219	109.0	1853	0.0976	0.9955	0.9955	0.9955
0.0216	110.0	1870	0.0969	0.9955	0.9955	0.9955
0.0211	111.0	1887	0.0957	0.9955	0.9955	0.9955
0.0211	112.0	1904	0.0946	0.9955	0.9955	0.9955
0.0203	113.0	1921	0.0937	0.9955	0.9955	0.9955
0.021	114.0	1938	0.0928	0.9955	0.9955	0.9955
0.0206	115.0	1955	0.0920	0.9955	0.9955	0.9955
0.0212	116.0	1972	0.0916	0.9955	0.9955	0.9955
0.0201	117.0	1989	0.0906	0.9955	0.9955	0.9955
0.0195	118.0	2006	0.0896	0.9955	0.9955	0.9955
0.0185	119.0	2023	0.0887	0.9955	0.9955	0.9955
0.0188	120.0	2040	0.0880	0.9955	0.9955	0.9955
0.0196	121.0	2057	0.0875	0.9955	0.9955	0.9955
0.0179	122.0	2074	0.0866	0.9955	0.9955	0.9955
0.0175	123.0	2091	0.0860	0.9955	0.9955	0.9955
0.0175	124.0	2108	0.0852	0.9955	0.9955	0.9955
0.0184	125.0	2125	0.0844	0.9955	0.9955	0.9955
0.0179	126.0	2142	0.0836	0.9955	0.9955	0.9955
0.0171	127.0	2159	0.0832	0.9955	0.9955	0.9955
0.0173	128.0	2176	0.0826	0.9955	0.9955	0.9955
0.0169	129.0	2193	0.0818	0.9955	0.9955	0.9955
0.0163	130.0	2210	0.0813	0.9955	0.9955	0.9955
0.0164	131.0	2227	0.0803	0.9955	0.9955	0.9955
0.0167	132.0	2244	0.0801	0.9955	0.9955	0.9955
0.0162	133.0	2261	0.0796	0.9955	0.9955	0.9955
0.015	134.0	2278	0.0790	0.9955	0.9955	0.9955
0.0153	135.0	2295	0.0785	0.9955	0.9955	0.9955
0.0147	136.0	2312	0.0780	0.9955	0.9955	0.9955
0.0147	137.0	2329	0.0772	0.9955	0.9955	0.9955
0.0151	138.0	2346	0.0770	0.9955	0.9955	0.9955
0.0143	139.0	2363	0.0762	0.9955	0.9955	0.9955
0.0149	140.0	2380	0.0754	0.9955	0.9955	0.9955
0.0143	141.0	2397	0.0752	0.9955	0.9955	0.9955
0.0145	142.0	2414	0.0747	0.9955	0.9955	0.9955
0.014	143.0	2431	0.0740	0.9955	0.9955	0.9955
0.0138	144.0	2448	0.0738	0.9955	0.9955	0.9955
0.0138	145.0	2465	0.0733	0.9955	0.9955	0.9955
0.0127	146.0	2482	0.0729	0.9955	0.9955	0.9955
0.013	147.0	2499	0.0726	0.9955	0.9955	0.9955
0.0135	148.0	2516	0.0722	0.9955	0.9955	0.9955
0.0135	149.0	2533	0.0716	0.9955	0.9955	0.9955
0.0137	150.0	2550	0.0711	0.9955	0.9955	0.9955
0.0138	151.0	2567	0.0707	0.9955	0.9955	0.9955
0.013	152.0	2584	0.0703	0.9955	0.9955	0.9955
0.0128	153.0	2601	0.0698	0.9955	0.9955	0.9955
0.0129	154.0	2618	0.0696	0.9955	0.9955	0.9955
0.0121	155.0	2635	0.0690	0.9955	0.9955	0.9955
0.0127	156.0	2652	0.0687	0.9955	0.9955	0.9955
0.0126	157.0	2669	0.0685	0.9955	0.9955	0.9955
0.0124	158.0	2686	0.0681	0.9955	0.9955	0.9955
0.0118	159.0	2703	0.0675	0.9955	0.9955	0.9955
0.0119	160.0	2720	0.0673	0.9955	0.9955	0.9955
0.0119	161.0	2737	0.0672	0.9955	0.9955	0.9955
0.0116	162.0	2754	0.0667	0.9955	0.9955	0.9955
0.0119	163.0	2771	0.0664	0.9955	0.9955	0.9955
0.0112	164.0	2788	0.0662	0.9955	0.9955	0.9955
0.0112	165.0	2805	0.0656	0.9955	0.9955	0.9955
0.0115	166.0	2822	0.0653	0.9955	0.9955	0.9955
0.0111	167.0	2839	0.0651	0.9955	0.9955	0.9955
0.0107	168.0	2856	0.0649	0.9955	0.9955	0.9955
0.0115	169.0	2873	0.0645	0.9955	0.9955	0.9955
0.0111	170.0	2890	0.0643	0.9955	0.9955	0.9955
0.0109	171.0	2907	0.0639	0.9955	0.9955	0.9955
0.0107	172.0	2924	0.0637	0.9955	0.9955	0.9955
0.0108	173.0	2941	0.0635	0.9955	0.9955	0.9955
0.0103	174.0	2958	0.0631	0.9955	0.9955	0.9955
0.0104	175.0	2975	0.0627	0.9955	0.9955	0.9955
0.0104	176.0	2992	0.0626	0.9955	0.9955	0.9955
0.0104	177.0	3009	0.0622	0.9955	0.9955	0.9955
0.0102	178.0	3026	0.0621	0.9955	0.9955	0.9955
0.0103	179.0	3043	0.0618	0.9955	0.9955	0.9955
0.0096	180.0	3060	0.0617	0.9955	0.9955	0.9955
0.0097	181.0	3077	0.0613	0.9955	0.9955	0.9955
0.0099	182.0	3094	0.0611	0.9955	0.9955	0.9955
0.0097	183.0	3111	0.0610	0.9955	0.9955	0.9955
0.0099	184.0	3128	0.0606	0.9955	0.9955	0.9955
0.0098	185.0	3145	0.0605	0.9955	0.9955	0.9955
0.0095	186.0	3162	0.0603	0.9955	0.9955	0.9955
0.0096	187.0	3179	0.0600	0.9955	0.9955	0.9955
0.0096	188.0	3196	0.0598	0.9955	0.9955	0.9955
0.0094	189.0	3213	0.0595	0.9955	0.9955	0.9955
0.0095	190.0	3230	0.0596	0.9955	0.9955	0.9955
0.0092	191.0	3247	0.0592	0.9955	0.9955	0.9955
0.01	192.0	3264	0.0590	0.9955	0.9955	0.9955
0.0086	193.0	3281	0.0588	0.9955	0.9955	0.9955
0.0091	194.0	3298	0.0586	0.9955	0.9955	0.9955
0.0085	195.0	3315	0.0585	0.9955	0.9955	0.9955
0.0086	196.0	3332	0.0583	0.9955	0.9955	0.9955
0.009	197.0	3349	0.0582	0.9955	0.9955	0.9955
0.0083	198.0	3366	0.0579	0.9955	0.9955	0.9955
0.0087	199.0	3383	0.0578	0.9955	0.9955	0.9955
0.0087	200.0	3400	0.0576	0.9955	0.9955	0.9955
0.0084	201.0	3417	0.0574	0.9955	0.9955	0.9955
0.0089	202.0	3434	0.0573	0.9955	0.9955	0.9955
0.0092	203.0	3451	0.0571	0.9955	0.9955	0.9955
0.0083	204.0	3468	0.0569	0.9955	0.9955	0.9955
0.009	205.0	3485	0.0569	0.9955	0.9955	0.9955
0.0085	206.0	3502	0.0566	0.9955	0.9955	0.9955
0.0085	207.0	3519	0.0566	0.9955	0.9955	0.9955
0.0086	208.0	3536	0.0564	0.9955	0.9955	0.9955
0.0083	209.0	3553	0.0563	0.9955	0.9955	0.9955
0.0079	210.0	3570	0.0561	0.9955	0.9955	0.9955
0.008	211.0	3587	0.0560	0.9955	0.9955	0.9955
0.0087	212.0	3604	0.0559	0.9955	0.9955	0.9955
0.0083	213.0	3621	0.0557	0.9955	0.9955	0.9955
0.0083	214.0	3638	0.0557	0.9955	0.9955	0.9955
0.0081	215.0	3655	0.0556	0.9955	0.9955	0.9955
0.0081	216.0	3672	0.0555	0.9955	0.9955	0.9955
0.0081	217.0	3689	0.0553	0.9955	0.9955	0.9955
0.008	218.0	3706	0.0552	0.9955	0.9955	0.9955
0.008	219.0	3723	0.0551	0.9955	0.9955	0.9955
0.0082	220.0	3740	0.0550	0.9955	0.9955	0.9955
0.0079	221.0	3757	0.0549	0.9955	0.9955	0.9955
0.0076	222.0	3774	0.0548	0.9955	0.9955	0.9955
0.0074	223.0	3791	0.0547	0.9955	0.9955	0.9955
0.0073	224.0	3808	0.0547	0.9955	0.9955	0.9955
0.0083	225.0	3825	0.0546	0.9955	0.9955	0.9955
0.0079	226.0	3842	0.0545	0.9955	0.9955	0.9955
0.0075	227.0	3859	0.0544	0.9955	0.9955	0.9955
0.0077	228.0	3876	0.0543	0.9955	0.9955	0.9955
0.0076	229.0	3893	0.0543	0.9955	0.9955	0.9955
0.0075	230.0	3910	0.0541	0.9955	0.9955	0.9955
0.0078	231.0	3927	0.0541	0.9955	0.9955	0.9955
0.0077	232.0	3944	0.0540	0.9955	0.9955	0.9955
0.0078	233.0	3961	0.0540	0.9955	0.9955	0.9955
0.0075	234.0	3978	0.0539	0.9955	0.9955	0.9955
0.0073	235.0	3995	0.0539	0.9955	0.9955	0.9955
0.0078	236.0	4012	0.0538	0.9955	0.9955	0.9955
0.0077	237.0	4029	0.0538	0.9955	0.9955	0.9955
0.0074	238.0	4046	0.0538	0.9955	0.9955	0.9955
0.0073	239.0	4063	0.0537	0.9955	0.9955	0.9955
0.0077	240.0	4080	0.0537	0.9955	0.9955	0.9955
0.0075	241.0	4097	0.0537	0.9955	0.9955	0.9955
0.0075	242.0	4114	0.0536	0.9955	0.9955	0.9955
0.007	243.0	4131	0.0536	0.9955	0.9955	0.9955
0.0075	244.0	4148	0.0536	0.9955	0.9955	0.9955
0.0074	245.0	4165	0.0535	0.9955	0.9955	0.9955
0.0078	246.0	4182	0.0535	0.9955	0.9955	0.9955
0.0078	247.0	4199	0.0535	0.9955	0.9955	0.9955
0.0075	248.0	4216	0.0535	0.9955	0.9955	0.9955
0.0074	249.0	4233	0.0535	0.9955	0.9955	0.9955
0.0075	250.0	4250	0.0535	0.9955	0.9955	0.9955

Framework versions

• Transformers 4.57.1
• Pytorch 2.5.1+cu121
• Datasets 4.4.1
• Tokenizers 0.22.1