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--- |
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license: apache-2.0 |
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base_model: barghavani/Cheese_xray |
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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: Cheese_xray |
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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: test |
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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: 0.8883161512027491 |
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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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# Cheese_xray |
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This model is a fine-tuned version of [barghavani/Cheese_xray](https://huggingface.co/barghavani/Cheese_xray) on the chest-xray-classification dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2827 |
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- Accuracy: 0.8883 |
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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: 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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| 0.3993 | 0.99 | 63 | 0.4364 | 0.7165 | |
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| 0.3454 | 1.99 | 127 | 0.3947 | 0.7680 | |
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| 0.3327 | 3.0 | 191 | 0.3582 | 0.8591 | |
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| 0.3329 | 4.0 | 255 | 0.3371 | 0.8746 | |
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| 0.2992 | 4.99 | 318 | 0.3449 | 0.8643 | |
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| 0.3289 | 5.99 | 382 | 0.3172 | 0.8832 | |
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| 0.3309 | 7.0 | 446 | 0.2956 | 0.8935 | |
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| 0.2875 | 8.0 | 510 | 0.2911 | 0.8883 | |
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| 0.2764 | 8.99 | 573 | 0.2884 | 0.9124 | |
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| 0.265 | 9.88 | 630 | 0.2827 | 0.8883 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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