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dit-base-finetuned-brs

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dit-base-finetuned-brs

This model is a fine-tuned version of microsoft/dit-base on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8748
  • Accuracy: 0.8824
  • F1: 0.8571
  • Precision (ppv): 0.8571
  • Recall (sensitivity): 0.8571
  • Specificity: 0.9
  • Npv: 0.9
  • Auc: 0.8786

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: 1e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision (ppv) Recall (sensitivity) Specificity Npv Auc
0.6624 6.25 100 0.5548 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.5201 12.49 200 0.4617 0.8824 0.8571 0.8571 0.8571 0.9 0.9 0.8786
0.5172 18.74 300 0.4249 0.8235 0.8000 0.75 0.8571 0.8 0.8889 0.8286
0.4605 24.98 400 0.3172 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.4894 31.25 500 0.4466 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.3694 37.49 600 0.5077 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.6172 43.74 700 0.5722 0.7647 0.7143 0.7143 0.7143 0.8 0.8 0.7571
0.3671 49.98 800 0.7006 0.7647 0.6667 0.8 0.5714 0.9 0.75 0.7357
0.4109 56.25 900 0.4410 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.3198 62.49 1000 0.7226 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.4283 68.74 1100 0.8089 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.3273 74.98 1200 0.9059 0.7647 0.6667 0.8 0.5714 0.9 0.75 0.7357
0.3237 81.25 1300 0.8520 0.8235 0.7692 0.8333 0.7143 0.9 0.8182 0.8071
0.2014 87.49 1400 0.9183 0.7647 0.6667 0.8 0.5714 0.9 0.75 0.7357
0.3204 93.74 1500 0.6769 0.8824 0.8571 0.8571 0.8571 0.9 0.9 0.8786
0.1786 99.98 1600 0.8748 0.8824 0.8571 0.8571 0.8571 0.9 0.9 0.8786

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1
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Evaluation results

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