vit-beta1-0.95

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: 0.5187
• Accuracy: 0.8384
• Precision: 0.8496
• Recall: 0.8384
• F1: 0.8421

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=(0.95,0.999) 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
1.7463	1.0	321	1.0838	0.6810	0.6594	0.6810	0.6416
1.2057	2.0	642	0.7826	0.7025	0.7172	0.7025	0.6909
1.0261	3.0	963	0.7040	0.7406	0.7119	0.7406	0.6962
1.0269	4.0	1284	0.6113	0.7573	0.7934	0.7573	0.7620
0.943	5.0	1605	0.7935	0.6848	0.7925	0.6848	0.7063
0.9677	6.0	1926	0.6434	0.7555	0.7996	0.7555	0.7690
0.875	7.0	2247	0.6981	0.7157	0.7962	0.7157	0.7344
0.7796	8.0	2568	0.7443	0.7115	0.8097	0.7115	0.7352
0.7308	9.0	2889	0.5311	0.8103	0.8217	0.8103	0.8125
0.7036	10.0	3210	0.5371	0.7892	0.8341	0.7892	0.8006
0.637	11.0	3531	0.6372	0.7850	0.8268	0.7850	0.7963
0.5668	12.0	3852	0.5850	0.8024	0.8279	0.8024	0.8109
0.4823	13.0	4173	0.6345	0.8284	0.8341	0.8284	0.8115
0.4808	14.0	4494	0.6609	0.7992	0.8328	0.7992	0.8038
0.4087	15.0	4815	0.6568	0.7791	0.8373	0.7791	0.7952
0.3579	16.0	5136	0.5462	0.8294	0.8510	0.8294	0.8364
0.3015	17.0	5457	0.5187	0.8384	0.8496	0.8384	0.8421
0.2794	18.0	5778	0.6125	0.8204	0.8538	0.8204	0.8300
0.2297	19.0	6099	0.5672	0.8391	0.8500	0.8391	0.8429
0.229	20.0	6420	0.6051	0.8526	0.8543	0.8526	0.8524
0.1743	21.0	6741	0.5990	0.8606	0.8598	0.8606	0.8577
0.1327	22.0	7062	0.6290	0.8568	0.8636	0.8568	0.8590
0.1321	23.0	7383	0.6259	0.8655	0.8613	0.8655	0.8625
0.1236	24.0	7704	0.6135	0.8703	0.8699	0.8703	0.8697
0.126	25.0	8025	0.6107	0.8655	0.8658	0.8655	0.8645
0.0919	26.0	8346	0.6001	0.8700	0.8657	0.8700	0.8665
0.1421	27.0	8667	0.5894	0.8745	0.8721	0.8745	0.8727

Framework versions

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