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Wound-Image-classification

Image Classification
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vit
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  1. README.md +10 -36
  2. model.safetensors +1 -1
  3. runs/Mar11_10-29-46_33467ebf2898/events.out.tfevents.1710152987.33467ebf2898.370.9 +2 -2
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3723
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- - Accuracy: 0.8966
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  ## Model description
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@@ -37,7 +37,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 4
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  - eval_batch_size: 1
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  - seed: 42
@@ -46,44 +46,18 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 32
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.9629 | 0.98 | 21 | 1.8499 | 0.3908 |
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- | 1.6285 | 2.0 | 43 | 1.5155 | 0.5402 |
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- | 1.2198 | 2.98 | 64 | 1.0158 | 0.8161 |
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- | 0.7367 | 4.0 | 86 | 0.7362 | 0.8736 |
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- | 0.4858 | 4.98 | 107 | 0.5636 | 0.8621 |
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- | 0.3395 | 6.0 | 129 | 0.5547 | 0.8621 |
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- | 0.2782 | 6.98 | 150 | 0.5086 | 0.8276 |
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- | 0.2215 | 8.0 | 172 | 0.5234 | 0.8391 |
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- | 0.275 | 8.98 | 193 | 0.4102 | 0.8966 |
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- | 0.1995 | 10.0 | 215 | 0.5127 | 0.8621 |
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- | 0.2395 | 10.98 | 236 | 0.4507 | 0.8506 |
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- | 0.129 | 12.0 | 258 | 0.3436 | 0.8506 |
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- | 0.166 | 12.98 | 279 | 0.6932 | 0.8276 |
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- | 0.1156 | 14.0 | 301 | 0.4058 | 0.8851 |
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- | 0.1466 | 14.98 | 322 | 0.3980 | 0.8621 |
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- | 0.1673 | 16.0 | 344 | 0.3390 | 0.9195 |
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- | 0.0963 | 16.98 | 365 | 0.3953 | 0.8966 |
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- | 0.1058 | 18.0 | 387 | 0.4769 | 0.8621 |
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- | 0.1016 | 18.98 | 408 | 0.2004 | 0.9540 |
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- | 0.0792 | 20.0 | 430 | 0.2843 | 0.9310 |
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- | 0.0858 | 20.98 | 451 | 0.3540 | 0.8966 |
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- | 0.1046 | 22.0 | 473 | 0.5916 | 0.8276 |
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- | 0.0763 | 22.98 | 494 | 0.5639 | 0.8161 |
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- | 0.0934 | 24.0 | 516 | 0.4209 | 0.8736 |
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- | 0.1534 | 24.98 | 537 | 0.3895 | 0.8736 |
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- | 0.0785 | 26.0 | 559 | 0.4623 | 0.8391 |
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- | 0.0905 | 26.98 | 580 | 0.3779 | 0.8966 |
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- | 0.0589 | 28.0 | 602 | 0.3099 | 0.8851 |
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- | 0.0944 | 28.98 | 623 | 0.2375 | 0.9310 |
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- | 0.0567 | 30.0 | 645 | 0.2871 | 0.9195 |
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- | 0.0667 | 30.98 | 666 | 0.3315 | 0.9080 |
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- | 0.0631 | 31.26 | 672 | 0.3723 | 0.8966 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5677
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+ - Accuracy: 0.7701
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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  - train_batch_size: 4
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  - eval_batch_size: 1
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  - seed: 42
 
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 16
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.8404 | 0.98 | 21 | 1.5497 | 0.4713 |
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+ | 1.0897 | 2.0 | 43 | 1.0340 | 0.6552 |
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+ | 0.6793 | 2.98 | 64 | 0.6132 | 0.8851 |
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+ | 0.4211 | 4.0 | 86 | 0.5212 | 0.8506 |
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+ | 0.3818 | 4.98 | 107 | 0.5849 | 0.8276 |
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+ | 0.2636 | 6.0 | 129 | 0.5677 | 0.7701 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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