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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-patch16-224-finetuned-ISIC-dec2024gray |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9369401906963305 |
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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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# vit-base-patch16-224-finetuned-ISIC-dec2024gray |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1549 |
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- Accuracy: 0.9369 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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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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| 0.7609 | 1.0 | 974 | 0.1860 | 0.9231 | |
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| 0.5981 | 2.0 | 1948 | 0.1640 | 0.9320 | |
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| 0.5217 | 2.9974 | 2919 | 0.1549 | 0.9369 | |
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### Testing results |
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- True positive: 1341 |
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- False positive: 290 |
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- True negative: 14923 |
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- False negative: 750 |
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- Precision: 0.822 |
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- Recall: 0.641 |
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- Accuracy: 0.940 |
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- F1: 0.721 |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.6.0.dev20241225+cu126 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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