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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-large-patch4-window12-384-in22k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: microsoft/swin-large-patch4-window12-384-in22k |
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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: NIH-Xray |
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type: imagefolder |
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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: 0.49376114081996436 |
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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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# microsoft/swin-large-patch4-window12-384-in22k |
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This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384-in22k](https://huggingface.co./microsoft/swin-large-patch4-window12-384-in22k) on the NIH-Xray dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.7711 |
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- Accuracy: 0.4938 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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| 1.8318 | 0.9984 | 315 | 1.7651 | 0.5437 | |
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| 1.6067 | 2.0 | 631 | 1.6393 | 0.5455 | |
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| 1.406 | 2.9984 | 946 | 1.6472 | 0.5490 | |
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| 1.3983 | 4.0 | 1262 | 1.7344 | 0.5455 | |
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| 0.7272 | 4.9984 | 1577 | 2.1283 | 0.5258 | |
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| 0.3975 | 6.0 | 1893 | 2.5229 | 0.5134 | |
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| 0.2648 | 6.9984 | 2208 | 3.0333 | 0.5080 | |
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| 0.1232 | 8.0 | 2524 | 3.4626 | 0.5241 | |
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| 0.0873 | 8.9984 | 2839 | 3.6219 | 0.5027 | |
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| 0.0554 | 9.9842 | 3150 | 3.7711 | 0.4938 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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