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README.md
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---
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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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- image_folder
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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-SkinDisease
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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: image_folder
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type: image_folder
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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: 0.9342629482071713
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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-SkinDisease
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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 image_folder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1992
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- Accuracy: 0.9343
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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: 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: 10
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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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| 0.9099 | 1.0 | 282 | 0.8248 | 0.7647 |
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| 0.5848 | 2.0 | 565 | 0.4236 | 0.8748 |
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| 0.3952 | 3.0 | 847 | 0.3154 | 0.9021 |
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| 0.3957 | 4.0 | 1130 | 0.2695 | 0.9106 |
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| 0.3146 | 5.0 | 1412 | 0.2381 | 0.9198 |
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| 0.2883 | 6.0 | 1695 | 0.2407 | 0.9218 |
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| 0.2264 | 7.0 | 1977 | 0.2160 | 0.9278 |
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| 0.2339 | 8.0 | 2260 | 0.2121 | 0.9283 |
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| 0.1966 | 9.0 | 2542 | 0.2044 | 0.9303 |
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| 0.2366 | 9.98 | 2820 | 0.1992 | 0.9343 |
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### Framework versions
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- Transformers 4.33.2
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- Pytorch 2.0.0
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- Datasets 2.1.0
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- Tokenizers 0.13.3
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