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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-xray-pneumonia-classification
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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: 0.9742489270386266
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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-xray-pneumonia-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 the chest-xray-classification dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0868
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- Accuracy: 0.9742
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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: 15
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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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| 0.5152 | 0.99 | 63 | 0.2507 | 0.9245 |
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| 0.2334 | 1.99 | 127 | 0.1766 | 0.9382 |
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| 0.1647 | 3.0 | 191 | 0.1218 | 0.9588 |
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| 0.144 | 4.0 | 255 | 0.1222 | 0.9502 |
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| 0.1348 | 4.99 | 318 | 0.1293 | 0.9571 |
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| 0.1276 | 5.99 | 382 | 0.1000 | 0.9665 |
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| 0.1175 | 7.0 | 446 | 0.1177 | 0.9502 |
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| 0.109 | 8.0 | 510 | 0.1079 | 0.9665 |
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| 0.0914 | 8.99 | 573 | 0.0804 | 0.9717 |
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| 0.0872 | 9.99 | 637 | 0.0800 | 0.9717 |
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| 0.0804 | 11.0 | 701 | 0.0862 | 0.9682 |
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| 0.0935 | 12.0 | 765 | 0.0883 | 0.9657 |
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| 0.0686 | 12.99 | 828 | 0.0868 | 0.9742 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 1.9.0+cu102
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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