vit-lr-0.0001

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: 0.4778
• Accuracy: 0.8311
• Precision: 0.8433
• Recall: 0.8311
• F1: 0.8269

Training procedure

Early stopping is employed with a patience of 10 and validation loss as the stopping criteria.

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
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6718	1.0	321	0.4934	0.8242	0.8254	0.8242	0.8163
0.4045	2.0	642	0.4778	0.8311	0.8433	0.8311	0.8269
0.2419	3.0	963	0.5133	0.8405	0.8525	0.8405	0.8410
0.1267	4.0	1284	0.6154	0.8495	0.8491	0.8495	0.8448
0.0733	5.0	1605	0.7845	0.8422	0.8421	0.8422	0.8361
0.0446	6.0	1926	0.8767	0.8471	0.8407	0.8471	0.8408
0.0523	7.0	2247	0.8674	0.8450	0.8492	0.8450	0.8407
0.0388	8.0	2568	0.9754	0.8398	0.8566	0.8398	0.8387
0.0402	9.0	2889	0.9370	0.8492	0.8547	0.8492	0.8461
0.0283	10.0	3210	0.9218	0.8509	0.8496	0.8509	0.8483
0.0451	11.0	3531	0.9872	0.8474	0.8400	0.8474	0.8401
0.0549	12.0	3852	1.0203	0.8457	0.8488	0.8457	0.8416

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2