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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-chest-xray |
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results: [] |
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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-chest-xray |
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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 trpakov/chest-xray-classification dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0856 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: linear |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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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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| 0.1891 | 0.1307 | 100 | 0.1028 | 0.9665 | |
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| 0.2123 | 0.2614 | 200 | 0.1254 | 0.9562 | |
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| 0.0536 | 0.3922 | 300 | 0.1142 | 0.9691 | |
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| 0.0799 | 0.5229 | 400 | 0.1173 | 0.9648 | |
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| 0.0537 | 0.6536 | 500 | 0.0856 | 0.9742 | |
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| 0.0911 | 0.7843 | 600 | 0.2005 | 0.9425 | |
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| 0.1027 | 0.9150 | 700 | 0.0869 | 0.9708 | |
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| 0.1011 | 1.0458 | 800 | 0.1063 | 0.9631 | |
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| 0.0717 | 1.1765 | 900 | 0.1424 | 0.9588 | |
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| 0.0605 | 1.3072 | 1000 | 0.1525 | 0.9648 | |
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| 0.0573 | 1.4379 | 1100 | 0.0970 | 0.9700 | |
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| 0.024 | 1.5686 | 1200 | 0.0867 | 0.9751 | |
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| 0.0056 | 1.6993 | 1300 | 0.0888 | 0.9760 | |
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| 0.0051 | 1.8301 | 1400 | 0.1054 | 0.9768 | |
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| 0.063 | 1.9608 | 1500 | 0.1896 | 0.9571 | |
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| 0.002 | 2.0915 | 1600 | 0.1886 | 0.9588 | |
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| 0.005 | 2.2222 | 1700 | 0.1184 | 0.9734 | |
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| 0.0083 | 2.3529 | 1800 | 0.1084 | 0.9760 | |
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| 0.0013 | 2.4837 | 1900 | 0.0903 | 0.9777 | |
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| 0.0298 | 2.6144 | 2000 | 0.1023 | 0.9734 | |
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| 0.0008 | 2.7451 | 2100 | 0.1104 | 0.9768 | |
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| 0.0011 | 2.8758 | 2200 | 0.1128 | 0.9785 | |
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| 0.0006 | 3.0065 | 2300 | 0.1395 | 0.9734 | |
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| 0.0059 | 3.1373 | 2400 | 0.1419 | 0.9725 | |
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| 0.0005 | 3.2680 | 2500 | 0.1335 | 0.9777 | |
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| 0.0005 | 3.3987 | 2600 | 0.1249 | 0.9768 | |
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| 0.0007 | 3.5294 | 2700 | 0.1157 | 0.9777 | |
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| 0.0005 | 3.6601 | 2800 | 0.1202 | 0.9785 | |
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| 0.001 | 3.7908 | 2900 | 0.1239 | 0.9777 | |
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| 0.0004 | 3.9216 | 3000 | 0.1231 | 0.9768 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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