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Hemg/Wound-Image-classification

🤗 Hugging Face sourceimage-classificationapache-2.086M params343 MBsafetensors✓ 39 checksumsupdated today
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Wound-Image-classification

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

  • Loss: 0.1209
  • Accuracy: 0.965

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 16

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0919 1.0 200 0.7780 0.76
0.6157 2.0 400 0.5695 0.7925
0.4894 3.0 600 0.3667 0.8775
0.3786 4.0 800 0.4436 0.8625
0.3142 5.0 1000 0.4412 0.8625
0.2636 6.0 1200 0.4430 0.86
0.198 7.0 1400 0.2760 0.9175
0.1456 8.0 1600 0.2211 0.93
0.1586 9.0 1800 0.3520 0.905
0.1307 10.0 2000 0.3188 0.9175
0.106 11.0 2200 0.3167 0.925
0.0975 12.0 2400 0.2633 0.92
0.0734 13.0 2600 0.1813 0.9525
0.0994 14.0 2800 0.2150 0.945
0.0622 15.0 3000 0.1757 0.955
0.0609 16.0 3200 0.1209 0.965

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2