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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- chest-xray-classification
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metrics:
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- accuracy
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model-index:
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- name: vit-pneumonia
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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: chest-xray-classification
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type: chest-xray-classification
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config: full
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split: validation
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args: full
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.976824034334764
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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-pneumonia
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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 the chest-xray-classification dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1086
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- Accuracy: 0.9768
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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: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.25
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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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| 0.0357 | 1.0 | 192 | 0.0955 | 0.9691 |
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| 0.0404 | 2.0 | 384 | 0.0720 | 0.9751 |
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| 0.0546 | 3.0 | 576 | 0.2275 | 0.9468 |
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| 0.0113 | 4.0 | 768 | 0.1386 | 0.9648 |
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| 0.0101 | 5.0 | 960 | 0.1212 | 0.9708 |
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| 0.0003 | 6.0 | 1152 | 0.0929 | 0.9777 |
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| 0.0002 | 7.0 | 1344 | 0.1051 | 0.9777 |
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| 0.0002 | 8.0 | 1536 | 0.1075 | 0.9777 |
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| 0.0002 | 9.0 | 1728 | 0.1084 | 0.9768 |
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| 0.0002 | 10.0 | 1920 | 0.1086 | 0.9768 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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