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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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- keremberke/chest-xray-classification |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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base_model: google/vit-base-patch16-224-in21k |
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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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type: image-classification |
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name: 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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- type: accuracy |
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value: 0.9742489270386266 |
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name: Accuracy |
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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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## Inference example |
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```python |
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from transformers import pipeline |
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classifier = pipeline(model="lxyuan/vit-xray-pneumonia-classification") |
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# image taken from https://www.news-medical.net/health/What-is-Viral-Pneumonia.aspx |
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classifier("https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20200618040600/ri/650/picture/2020/6/shutterstock_786937069.jpg") |
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>>> |
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[{'score': 0.990334689617157, 'label': 'PNEUMONIA'}, |
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{'score': 0.009665317833423615, 'label': 'NORMAL'}] |
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``` |
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## Training procedure |
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Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/ViT-xray-classification.ipynb) |
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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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```python |
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from transformers import EarlyStoppingCallback |
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training_args = TrainingArguments( |
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output_dir="vit-xray-pneumonia-classification", |
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remove_unused_columns=False, |
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evaluation_strategy="epoch", |
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save_strategy="epoch", |
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logging_strategy="epoch", |
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learning_rate=5e-5, |
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per_device_train_batch_size=16, |
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gradient_accumulation_steps=4, |
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per_device_eval_batch_size=16, |
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num_train_epochs=15, |
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save_total_limit=2, |
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warmup_ratio=0.1, |
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load_best_model_at_end=True, |
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metric_for_best_model="eval_loss", |
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greater_is_better=False, |
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fp16=True, |
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push_to_hub=True, |
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report_to="tensorboard" |
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) |
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early_stopping = EarlyStoppingCallback(early_stopping_patience=3) |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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data_collator=data_collator, |
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train_dataset=train_ds, |
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eval_dataset=val_ds, |
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tokenizer=processor, |
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compute_metrics=compute_metrics, |
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callbacks=[early_stopping], |
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) |
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``` |
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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 |
