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: 0.5000
Accuracy: 0.8519
Precision: 0.8575
Recall: 0.8519
F1: 0.8527

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=(0.9,0.9995) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1733
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.7395	1.0	321	0.9629	0.6845	0.6040	0.6845	0.5948
1.1477	2.0	642	0.7630	0.7174	0.7438	0.7174	0.7138
1.0466	3.0	963	0.6211	0.7660	0.7543	0.7660	0.7373
0.9366	4.0	1284	0.6310	0.7354	0.7934	0.7354	0.7518
0.9629	5.0	1605	0.5584	0.7982	0.7934	0.7982	0.7865
0.9753	6.0	1926	0.5606	0.7920	0.7922	0.7920	0.7916
0.8629	7.0	2247	0.7154	0.6969	0.8040	0.6969	0.7238
0.7817	8.0	2568	0.6276	0.7597	0.8105	0.7597	0.7743
0.7355	9.0	2889	0.5259	0.8110	0.8215	0.8110	0.8133
0.6878	10.0	3210	0.5347	0.7885	0.8388	0.7885	0.8022
0.6206	11.0	3531	0.5543	0.7958	0.8375	0.7958	0.8079
0.5628	12.0	3852	0.6465	0.7819	0.8285	0.7819	0.7960
0.4607	13.0	4173	0.6070	0.8190	0.8411	0.8190	0.8195
0.4558	14.0	4494	0.6088	0.8138	0.8424	0.8138	0.8215
0.3984	15.0	4815	0.5507	0.8308	0.8486	0.8308	0.8343
0.3541	16.0	5136	0.5112	0.8412	0.8528	0.8412	0.8448
0.3203	17.0	5457	0.5000	0.8519	0.8575	0.8519	0.8527
0.2685	18.0	5778	0.5630	0.8429	0.8574	0.8429	0.8472
0.214	19.0	6099	0.5670	0.8405	0.8607	0.8405	0.8464
0.2195	20.0	6420	0.6209	0.8478	0.8557	0.8478	0.8500
0.1598	21.0	6741	0.5597	0.8630	0.8585	0.8630	0.8590
0.1174	22.0	7062	0.5682	0.8637	0.8617	0.8637	0.8624
0.1341	23.0	7383	0.6186	0.8662	0.8638	0.8662	0.8622
0.1307	24.0	7704	0.5961	0.8727	0.8710	0.8727	0.8701
0.1075	25.0	8025	0.5912	0.8738	0.8715	0.8738	0.8717
0.0741	26.0	8346	0.5991	0.8755	0.8711	0.8755	0.8713
0.1476	27.0	8667	0.5900	0.8745	0.8705	0.8745	0.8714

Framework versions

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

sharren/vit-beta2-0.9995

vit-beta2-0.9995

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sharren/
vit-beta2-0.9995