vit-beta1-0.85

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: 0.5102
• Accuracy: 0.8558
• Precision: 0.8568
• Recall: 0.8558
• F1: 0.8553

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=(0.85,0.999) 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
1.763	1.0	321	0.9505	0.6952	0.6346	0.6952	0.6202
1.149	2.0	642	0.7147	0.7445	0.7457	0.7445	0.7230
1.0452	3.0	963	0.6250	0.7573	0.7591	0.7573	0.7321
1.0048	4.0	1284	0.5614	0.7784	0.7792	0.7784	0.7737
0.931	5.0	1605	0.6082	0.7739	0.8020	0.7739	0.7823
0.9808	6.0	1926	0.5542	0.7982	0.7984	0.7982	0.7951
0.8908	7.0	2247	0.5957	0.7545	0.8202	0.7545	0.7709
0.7747	8.0	2568	0.5766	0.7694	0.8155	0.7694	0.7836
0.741	9.0	2889	0.5431	0.7996	0.8190	0.7996	0.8047
0.7179	10.0	3210	0.5865	0.7774	0.8313	0.7774	0.7904
0.6102	11.0	3531	0.5288	0.8096	0.8361	0.8096	0.8180
0.574	12.0	3852	0.5991	0.7996	0.8332	0.7996	0.8096
0.4515	13.0	4173	0.5890	0.8370	0.8334	0.8370	0.8293
0.4629	14.0	4494	0.5573	0.8121	0.8463	0.8121	0.8205
0.3927	15.0	4815	0.5279	0.8332	0.8506	0.8332	0.8357
0.3535	16.0	5136	0.5364	0.8356	0.8494	0.8356	0.8405
0.2635	17.0	5457	0.5475	0.8547	0.8626	0.8547	0.8532
0.2493	18.0	5778	0.5102	0.8558	0.8568	0.8558	0.8553
0.2125	19.0	6099	0.6120	0.8329	0.8623	0.8329	0.8418
0.2179	20.0	6420	0.5721	0.8568	0.8563	0.8568	0.8563
0.1598	21.0	6741	0.5503	0.8651	0.8623	0.8651	0.8633
0.1194	22.0	7062	0.5829	0.8679	0.8672	0.8679	0.8669
0.1245	23.0	7383	0.6138	0.8682	0.8632	0.8682	0.8629
0.1239	24.0	7704	0.6136	0.8731	0.8703	0.8731	0.8695
0.1159	25.0	8025	0.5931	0.8752	0.8724	0.8752	0.8726
0.089	26.0	8346	0.5847	0.8776	0.8743	0.8776	0.8750
0.1123	27.0	8667	0.5941	0.8752	0.8710	0.8752	0.8719
0.0779	28.0	8988	0.6038	0.8766	0.8722	0.8766	0.8729

Framework versions

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