vit-base-patch16-224-in21k-finetuned-biopsy

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

• Loss: 0.0838
• Accuracy: 0.9799

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1862	1.0	42	1.1167	0.5611
0.7235	2.0	84	0.6029	0.8543
0.4286	3.0	126	0.3452	0.9280
0.3612	4.0	168	0.3485	0.8945
0.3015	5.0	210	0.2590	0.9296
0.2917	6.0	252	0.2219	0.9414
0.2312	7.0	294	0.2400	0.9280
0.1708	8.0	336	0.2120	0.9414
0.1806	9.0	378	0.1784	0.9514
0.1703	10.0	420	0.1571	0.9481
0.139	11.0	462	0.1544	0.9648
0.1301	12.0	504	0.1431	0.9598
0.122	13.0	546	0.1297	0.9631
0.1104	14.0	588	0.1401	0.9598
0.1075	15.0	630	0.1200	0.9665
0.0986	16.0	672	0.1665	0.9581
0.092	17.0	714	0.1399	0.9531
0.1123	18.0	756	0.1122	0.9698
0.0766	19.0	798	0.1337	0.9564
0.0762	20.0	840	0.0974	0.9732
0.0994	21.0	882	0.1023	0.9698
0.0687	22.0	924	0.0976	0.9749
0.0767	23.0	966	0.0952	0.9765
0.0581	24.0	1008	0.1096	0.9665
0.0544	25.0	1050	0.1123	0.9715
0.079	26.0	1092	0.1040	0.9682
0.0661	27.0	1134	0.0838	0.9799
0.068	28.0	1176	0.1169	0.9715
0.0722	29.0	1218	0.0897	0.9732
0.048	30.0	1260	0.0864	0.9732
0.0509	31.0	1302	0.0858	0.9749
0.047	32.0	1344	0.0801	0.9782
0.0411	33.0	1386	0.1221	0.9648
0.0378	34.0	1428	0.1011	0.9648
0.0358	35.0	1470	0.0834	0.9799
0.0347	36.0	1512	0.0993	0.9715
0.0434	37.0	1554	0.0938	0.9732
0.0507	38.0	1596	0.0874	0.9782
0.0466	39.0	1638	0.0932	0.9765
0.0502	40.0	1680	0.1012	0.9698
0.0289	41.0	1722	0.0841	0.9715
0.0274	42.0	1764	0.0883	0.9682
0.0251	43.0	1806	0.0843	0.9782
0.0343	44.0	1848	0.0812	0.9782
0.0289	45.0	1890	0.0805	0.9782
0.0277	46.0	1932	0.0943	0.9698
0.0332	47.0	1974	0.0807	0.9765
0.0328	48.0	2016	0.0826	0.9749
0.0257	49.0	2058	0.0852	0.9749
0.0287	50.0	2100	0.0848	0.9782

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1