vit-base-patch16-224-in21k-finetuned-papsmear

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: 0.2825
• Accuracy: 0.9338

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
No log	0.9231	9	1.7346	0.2647
1.7645	1.9487	19	1.6152	0.3088
1.661	2.9744	29	1.4663	0.4118
1.496	4.0	39	1.2989	0.4853
1.3097	4.9231	48	1.1491	0.5588
1.091	5.9487	58	0.9933	0.7206
0.9088	6.9744	68	0.9171	0.6985
0.7858	8.0	78	0.8301	0.7721
0.7016	8.9231	87	0.7925	0.7353
0.6136	9.9487	97	0.6992	0.7647
0.532	10.9744	107	0.6401	0.8309
0.5018	12.0	117	0.5787	0.8382
0.4279	12.9231	126	0.6130	0.8088
0.4116	13.9487	136	0.5090	0.8382
0.3848	14.9744	146	0.5165	0.8676
0.3449	16.0	156	0.4843	0.8382
0.3008	16.9231	165	0.5460	0.8456
0.2797	17.9487	175	0.4985	0.8309
0.2696	18.9744	185	0.5586	0.8456
0.2633	20.0	195	0.4349	0.9044
0.2569	20.9231	204	0.4017	0.8897
0.27	21.9487	214	0.4758	0.8603
0.2706	22.9744	224	0.4133	0.8897
0.2211	24.0	234	0.3844	0.9118
0.1977	24.9231	243	0.3497	0.9265
0.1969	25.9487	253	0.3736	0.9044
0.1776	26.9744	263	0.3797	0.9044
0.1787	28.0	273	0.3949	0.8897
0.18	28.9231	282	0.3278	0.9265
0.1797	29.9487	292	0.3615	0.9044
0.1665	30.9744	302	0.4174	0.8603
0.163	32.0	312	0.3574	0.8971
0.1498	32.9231	321	0.3591	0.9044
0.1405	33.9487	331	0.3017	0.9191
0.155	34.9744	341	0.3303	0.9265
0.1519	36.0	351	0.3559	0.8971
0.1415	36.9231	360	0.2890	0.9191
0.1256	37.9487	370	0.3445	0.8897
0.1217	38.9744	380	0.3435	0.9118
0.1285	40.0	390	0.3025	0.9191
0.1285	40.9231	399	0.3602	0.8824
0.1301	41.9487	409	0.3336	0.8897
0.1243	42.9744	419	0.2825	0.9338
0.1191	44.0	429	0.2835	0.9265
0.1221	44.9231	438	0.2724	0.9191
0.1151	45.9487	448	0.2708	0.9191
0.1195	46.1538	450	0.2707	0.9191

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1