ViT_B16

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

• Loss: 0.0999
• Accuracy: 0.9729
• Precision: 0.9874
• Recall: 0.9536
• F1: 0.9702
• Tp: 1562
• Tn: 1890
• Fp: 20
• Fn: 76

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-06
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 276
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Tp	Tn	Fp	Fn
0.6533	0.2477	55	0.5250	0.8470	0.8296	0.8413	0.8354	1378	1627	283	260
0.4246	0.4955	110	0.3119	0.9081	0.9379	0.8578	0.8960	1405	1817	93	233
0.2834	0.7432	165	0.2395	0.9194	0.9033	0.9243	0.9137	1514	1748	162	124
0.2425	0.9910	220	0.1882	0.9369	0.9348	0.9280	0.9314	1520	1804	106	118
0.2127	1.2387	275	0.1657	0.9501	0.9551	0.9359	0.9454	1533	1838	72	105
0.1973	1.4865	330	0.1446	0.9580	0.9709	0.9371	0.9537	1535	1864	46	103
0.1943	1.7342	385	0.1417	0.9628	0.9772	0.9414	0.9590	1542	1874	36	96
0.1934	1.9820	440	0.1173	0.9696	0.9904	0.9432	0.9662	1545	1895	15	93
0.1671	2.2297	495	0.1085	0.9707	0.9968	0.9396	0.9673	1539	1905	5	99
0.1755	2.4775	550	0.1140	0.9713	0.9898	0.9475	0.9682	1552	1894	16	86
0.1836	2.7252	605	0.1238	0.9659	0.9720	0.9536	0.9627	1562	1865	45	76
0.1664	2.9730	660	0.1199	0.9667	0.975	0.9524	0.9636	1560	1870	40	78
0.1693	3.2207	715	0.1189	0.9679	0.9745	0.9554	0.9649	1565	1869	41	73
0.1646	3.4685	770	0.1073	0.9701	0.9867	0.9481	0.9670	1553	1889	21	85
0.1585	3.7162	825	0.1076	0.9687	0.9805	0.9512	0.9656	1558	1879	31	80
0.1604	3.9640	880	0.1054	0.9729	0.9892	0.9518	0.9701	1559	1893	17	79
0.1701	4.2117	935	0.1046	0.9704	0.9806	0.9548	0.9675	1564	1879	31	74
0.1607	4.4595	990	0.1039	0.9713	0.9830	0.9542	0.9684	1563	1883	27	75
0.1631	4.7072	1045	0.1010	0.9727	0.9873	0.9530	0.9699	1561	1890	20	77
0.1483	4.9550	1100	0.0999	0.9729	0.9874	0.9536	0.9702	1562	1890	20	76

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2