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Wound-classification

Image Classification
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vit
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  1. README.md +7 -23
  2. model.safetensors +1 -1
  3. runs/Mar20_06-11-53_d04eab29c3a2/events.out.tfevents.1710915114.d04eab29c3a2.379.3 +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: 0.2462
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- - Accuracy: 0.945
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  ## Model description
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@@ -46,32 +46,16 @@ 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: 0.01
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- - num_epochs: 20
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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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- | 1.1814 | 1.0 | 200 | 0.9930 | 0.6875 |
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- | 0.79 | 2.0 | 400 | 0.8904 | 0.7275 |
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- | 0.6328 | 3.0 | 600 | 0.6603 | 0.7775 |
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- | 0.5951 | 4.0 | 800 | 0.4529 | 0.855 |
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- | 0.4113 | 5.0 | 1000 | 0.5102 | 0.845 |
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- | 0.3954 | 6.0 | 1200 | 0.3596 | 0.8875 |
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- | 0.2992 | 7.0 | 1400 | 0.4398 | 0.875 |
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- | 0.2586 | 8.0 | 1600 | 0.5174 | 0.8725 |
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- | 0.2486 | 9.0 | 1800 | 0.5932 | 0.865 |
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- | 0.2315 | 10.0 | 2000 | 0.4132 | 0.8825 |
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- | 0.1957 | 11.0 | 2200 | 0.3367 | 0.9125 |
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- | 0.1588 | 12.0 | 2400 | 0.3929 | 0.9 |
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- | 0.1741 | 13.0 | 2600 | 0.2468 | 0.9425 |
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- | 0.1079 | 14.0 | 2800 | 0.3453 | 0.91 |
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- | 0.106 | 15.0 | 3000 | 0.3909 | 0.9175 |
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- | 0.0869 | 16.0 | 3200 | 0.2978 | 0.94 |
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- | 0.089 | 17.0 | 3400 | 0.2792 | 0.9425 |
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- | 0.0954 | 18.0 | 3600 | 0.1710 | 0.955 |
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- | 0.0792 | 19.0 | 3800 | 0.2537 | 0.92 |
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- | 0.0657 | 20.0 | 4000 | 0.2462 | 0.945 |
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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: 0.3209
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+ - Accuracy: 0.905
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  ## Model description
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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: 0.01
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+ - num_epochs: 4
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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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+ | 1.1491 | 1.0 | 200 | 0.8655 | 0.7425 |
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+ | 0.6448 | 2.0 | 400 | 0.5285 | 0.83 |
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+ | 0.4199 | 3.0 | 600 | 0.4229 | 0.8725 |
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+ | 0.2546 | 4.0 | 800 | 0.3209 | 0.905 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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