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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Wound-Image-classification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Wound-Image-classification |
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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: 0.1209 |
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- Accuracy: 0.965 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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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| 1.0919 | 1.0 | 200 | 0.7780 | 0.76 | |
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| 0.6157 | 2.0 | 400 | 0.5695 | 0.7925 | |
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| 0.4894 | 3.0 | 600 | 0.3667 | 0.8775 | |
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| 0.3786 | 4.0 | 800 | 0.4436 | 0.8625 | |
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| 0.3142 | 5.0 | 1000 | 0.4412 | 0.8625 | |
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| 0.2636 | 6.0 | 1200 | 0.4430 | 0.86 | |
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| 0.198 | 7.0 | 1400 | 0.2760 | 0.9175 | |
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| 0.1456 | 8.0 | 1600 | 0.2211 | 0.93 | |
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| 0.1586 | 9.0 | 1800 | 0.3520 | 0.905 | |
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| 0.1307 | 10.0 | 2000 | 0.3188 | 0.9175 | |
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| 0.106 | 11.0 | 2200 | 0.3167 | 0.925 | |
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| 0.0975 | 12.0 | 2400 | 0.2633 | 0.92 | |
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| 0.0734 | 13.0 | 2600 | 0.1813 | 0.9525 | |
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| 0.0994 | 14.0 | 2800 | 0.2150 | 0.945 | |
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| 0.0622 | 15.0 | 3000 | 0.1757 | 0.955 | |
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| 0.0609 | 16.0 | 3200 | 0.1209 | 0.965 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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