ahishamm / vit-base-16-thesis-demo-ISIC-binary-class

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Total runs: 7
24-hour runs: 0
7-day runs: -2
30-day runs: 0
Model's Last Updated: December 06 2023
image-classification

Introduction of vit-base-16-thesis-demo-ISIC-binary-class

Model Details of vit-base-16-thesis-demo-ISIC-binary-class

vit-base-16-thesis-demo-ISIC-binary-class

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ahishamm/isic_binary__enhanced_balanced dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1585
  • Accuracy: 0.9458
  • Recall: 0.9458
  • F1: 0.9458
  • Precision: 0.9458
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: 0.0002
  • 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: linear
  • num_epochs: 4
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Accuracy Recall F1 Precision
0.4477 0.27 50 0.5296 0.7444 0.7444 0.7444 0.7444
0.3011 0.54 100 0.3341 0.8722 0.8722 0.8722 0.8722
0.3254 0.82 150 0.3987 0.7903 0.7903 0.7903 0.7903
0.2986 1.09 200 0.3169 0.8694 0.8694 0.8694 0.8694
0.3341 1.36 250 0.3114 0.8597 0.8597 0.8597 0.8597
0.2659 1.63 300 0.2607 0.8833 0.8833 0.8833 0.8833
0.1987 1.9 350 0.2100 0.9111 0.9111 0.9111 0.9111
0.3515 2.17 400 0.2369 0.9097 0.9097 0.9097 0.9097
0.1316 2.45 450 0.2714 0.9014 0.9014 0.9014 0.9014
0.2213 2.72 500 0.1714 0.9361 0.9361 0.9361 0.9361
0.1115 2.99 550 0.1585 0.9458 0.9458 0.9458 0.9458
0.0377 3.26 600 0.2058 0.9403 0.9403 0.9403 0.9403
0.0779 3.53 650 0.2187 0.9375 0.9375 0.9375 0.9375
0.0444 3.8 700 0.2140 0.9389 0.9389 0.9389 0.9389
Framework versions
  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0

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