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Hemg
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Wound-classification

Image Classification
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Safetensors
vit
Generated from Trainer
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  1. README.md +19 -7
  2. model.safetensors +1 -1
  3. runs/Mar20_06-20-45_d04eab29c3a2/events.out.tfevents.1710915646.d04eab29c3a2.379.4 +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: 0.3209
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- - Accuracy: 0.905
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  ## Model description
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@@ -46,16 +46,28 @@ 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: 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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  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: 0.2523
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+ - Accuracy: 0.9425
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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: 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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+ | 1.2218 | 1.0 | 200 | 0.8285 | 0.745 |
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+ | 0.7855 | 2.0 | 400 | 0.7469 | 0.7525 |
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+ | 0.6213 | 3.0 | 600 | 0.5686 | 0.8025 |
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+ | 0.5695 | 4.0 | 800 | 0.5475 | 0.8225 |
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+ | 0.4172 | 5.0 | 1000 | 0.5988 | 0.825 |
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+ | 0.383 | 6.0 | 1200 | 0.4330 | 0.84 |
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+ | 0.271 | 7.0 | 1400 | 0.4766 | 0.845 |
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+ | 0.2281 | 8.0 | 1600 | 0.4235 | 0.87 |
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+ | 0.2283 | 9.0 | 1800 | 0.3102 | 0.91 |
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+ | 0.1632 | 10.0 | 2000 | 0.3234 | 0.9175 |
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+ | 0.1495 | 11.0 | 2200 | 0.4307 | 0.8925 |
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+ | 0.1504 | 12.0 | 2400 | 0.3205 | 0.92 |
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+ | 0.1372 | 13.0 | 2600 | 0.2476 | 0.9325 |
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+ | 0.0856 | 14.0 | 2800 | 0.3055 | 0.93 |
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+ | 0.0859 | 15.0 | 3000 | 0.2217 | 0.9375 |
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+ | 0.0682 | 16.0 | 3200 | 0.2523 | 0.9425 |
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  ### Framework versions
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