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vit-weight-decay-1e-2

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vit-weight-decay-1e-2

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: 0.4994
  • Accuracy: 0.8603
  • Precision: 0.8618
  • Recall: 0.8603
  • F1: 0.8600

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=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 1219
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.7124 1.0 321 0.8697 0.6924 0.6656 0.6924 0.6030
1.1476 2.0 642 0.7271 0.6990 0.7684 0.6990 0.7149
1.0734 3.0 963 0.6441 0.7687 0.7568 0.7687 0.7417
1.0271 4.0 1284 0.5855 0.7774 0.7883 0.7774 0.7814
0.9158 5.0 1605 0.7002 0.7635 0.7930 0.7635 0.7662
0.9167 6.0 1926 0.5867 0.7812 0.8065 0.7812 0.7900
0.786 7.0 2247 0.6517 0.7340 0.8047 0.7340 0.7515
0.7406 8.0 2568 0.6647 0.7067 0.8134 0.7067 0.7330
0.682 9.0 2889 0.5106 0.8228 0.8231 0.8228 0.8207
0.6427 10.0 3210 0.5032 0.8166 0.8354 0.8166 0.8222
0.5663 11.0 3531 0.5358 0.8152 0.8326 0.8152 0.8216
0.5395 12.0 3852 0.5488 0.8249 0.8392 0.8249 0.8299
0.4468 13.0 4173 0.5790 0.8232 0.8397 0.8232 0.8260
0.4247 14.0 4494 0.5438 0.8415 0.8570 0.8415 0.8449
0.3495 15.0 4815 0.5135 0.8454 0.8519 0.8454 0.8467
0.3039 16.0 5136 0.5631 0.8408 0.8520 0.8408 0.8448
0.2602 17.0 5457 0.4994 0.8603 0.8618 0.8603 0.8600
0.2616 18.0 5778 0.5406 0.8564 0.8622 0.8564 0.8585
0.1876 19.0 6099 0.5612 0.8481 0.8629 0.8481 0.8525
0.2052 20.0 6420 0.6803 0.8429 0.8502 0.8429 0.8428
0.1533 21.0 6741 0.5464 0.8734 0.8698 0.8734 0.8709
0.1175 22.0 7062 0.5573 0.8686 0.8667 0.8686 0.8673
0.1218 23.0 7383 0.6043 0.8703 0.8681 0.8703 0.8669
0.114 24.0 7704 0.5945 0.8710 0.8706 0.8710 0.8693
0.104 25.0 8025 0.5850 0.8766 0.8753 0.8766 0.8752
0.0752 26.0 8346 0.5868 0.8783 0.8747 0.8783 0.8757
0.1309 27.0 8667 0.5839 0.8786 0.8753 0.8786 0.8761

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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