vit-base-patch16-224-ve-U13b-R

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

• Loss: 0.3534
• Accuracy: 0.9348

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3157	0.99	51	1.2967	0.3478
0.9801	2.0	103	0.9966	0.5870
0.7385	2.99	154	0.7600	0.7174
0.572	4.0	206	0.6425	0.7826
0.3646	4.99	257	0.7687	0.6957
0.3033	6.0	309	0.6336	0.7391
0.3073	6.99	360	0.3534	0.9348
0.1623	8.0	412	0.8559	0.6739
0.1079	8.99	463	0.9730	0.7391
0.2703	10.0	515	0.7768	0.8043
0.178	10.99	566	0.8520	0.7826
0.2191	12.0	618	1.0049	0.7391
0.0597	12.99	669	0.8334	0.7609
0.0881	14.0	721	0.9985	0.7609
0.1265	14.99	772	0.9443	0.8043
0.0696	16.0	824	0.9878	0.8261
0.1198	16.99	875	0.8784	0.8043
0.1484	18.0	927	0.9595	0.7609
0.2887	18.99	978	1.0563	0.8043
0.1423	20.0	1030	0.8550	0.8043
0.083	20.99	1081	0.9093	0.7826
0.0695	22.0	1133	1.2758	0.6739
0.0285	22.99	1184	1.0852	0.7609
0.0132	24.0	1236	1.3341	0.6957
0.0957	24.99	1287	1.1965	0.7391
0.0633	26.0	1339	1.1199	0.7609
0.0705	26.99	1390	1.0551	0.8043
0.0564	28.0	1442	1.4332	0.7391
0.0798	28.99	1493	1.3855	0.7391
0.0326	30.0	1545	1.0534	0.8043
0.092	30.99	1596	1.1745	0.7609
0.1243	32.0	1648	1.1341	0.8043
0.062	32.99	1699	1.2648	0.7826
0.0941	34.0	1751	1.1236	0.7826
0.0119	34.99	1802	1.1303	0.8043
0.044	36.0	1854	1.1848	0.7826
0.0073	36.99	1905	1.1796	0.7609
0.0149	38.0	1957	1.2491	0.7826
0.0194	38.99	2008	1.1812	0.7826
0.0577	39.61	2040	1.1777	0.7609

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0