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vit-beta1-0.85

Image Classification
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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
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Evaluation results

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