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

Loss: 0.4899
Accuracy: 0.8592
Precision: 0.8593
Recall: 0.8592
F1: 0.8581

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: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1219
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.6642	1.0	321	0.8597	0.7178	0.6898	0.7178	0.6753
1.1191	2.0	642	0.6286	0.7656	0.7678	0.7656	0.7653
1.0741	3.0	963	0.6771	0.7545	0.7795	0.7545	0.7353
1.0023	4.0	1284	0.6269	0.7708	0.7839	0.7708	0.7727
0.92	5.0	1605	0.6010	0.7816	0.7913	0.7816	0.7811
0.9155	6.0	1926	0.5399	0.7944	0.8035	0.7944	0.7979
0.7971	7.0	2247	0.7016	0.7347	0.7903	0.7347	0.7509
0.7171	8.0	2568	0.6352	0.7396	0.8175	0.7396	0.7601
0.676	9.0	2889	0.5073	0.8072	0.8241	0.8072	0.8120
0.6312	10.0	3210	0.5552	0.7850	0.8374	0.7850	0.7978
0.5777	11.0	3531	0.5175	0.8173	0.8428	0.8173	0.8247
0.4686	12.0	3852	0.5712	0.8062	0.8413	0.8062	0.8172
0.4134	13.0	4173	0.6331	0.8214	0.8384	0.8214	0.8180
0.3904	14.0	4494	0.6161	0.8308	0.8546	0.8308	0.8357
0.3388	15.0	4815	0.5141	0.8384	0.8519	0.8384	0.8425
0.301	16.0	5136	0.5525	0.8360	0.8521	0.8360	0.8416
0.2516	17.0	5457	0.4899	0.8592	0.8593	0.8592	0.8581
0.2433	18.0	5778	0.5183	0.8509	0.8565	0.8509	0.8528
0.1769	19.0	6099	0.6016	0.8426	0.8622	0.8426	0.8487
0.179	20.0	6420	0.6330	0.8551	0.8543	0.8551	0.8534
0.1401	21.0	6741	0.5884	0.8669	0.8612	0.8669	0.8630
0.1085	22.0	7062	0.6131	0.8627	0.8591	0.8627	0.8603
0.1188	23.0	7383	0.6344	0.8648	0.8581	0.8648	0.8596
0.106	24.0	7704	0.6152	0.8731	0.8687	0.8731	0.8696
0.1029	25.0	8025	0.6209	0.8717	0.8676	0.8717	0.8692
0.0761	26.0	8346	0.6190	0.8727	0.8679	0.8727	0.8694
0.1207	27.0	8667	0.6154	0.8721	0.8677	0.8721	0.8693

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

sharren/vit-dropout-0.5

vit-dropout-0.5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sharren/
vit-dropout-0.5