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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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tags: |
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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: swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2 |
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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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# swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2 |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5483 |
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- Accuracy: 0.7715 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 30 |
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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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| 0.2349 | 0.9984 | 312 | 0.5575 | 0.7698 | |
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| 0.2191 | 2.0 | 625 | 0.5572 | 0.7618 | |
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| 0.2124 | 2.9984 | 937 | 0.5580 | 0.769 | |
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| 0.2207 | 4.0 | 1250 | 0.5500 | 0.763 | |
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| 0.2143 | 4.9984 | 1562 | 0.5575 | 0.7652 | |
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| 0.2191 | 6.0 | 1875 | 0.5486 | 0.7728 | |
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| 0.2063 | 6.9984 | 2187 | 0.5594 | 0.7615 | |
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| 0.207 | 8.0 | 2500 | 0.5405 | 0.7695 | |
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| 0.2273 | 8.9984 | 2812 | 0.5568 | 0.7672 | |
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| 0.2136 | 10.0 | 3125 | 0.5483 | 0.7728 | |
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| 0.2184 | 10.9984 | 3437 | 0.5606 | 0.7665 | |
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| 0.212 | 12.0 | 3750 | 0.5578 | 0.761 | |
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| 0.1903 | 12.9984 | 4062 | 0.5371 | 0.769 | |
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| 0.2487 | 14.0 | 4375 | 0.5582 | 0.7645 | |
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| 0.2025 | 14.9984 | 4687 | 0.5414 | 0.7778 | |
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| 0.2207 | 16.0 | 5000 | 0.5376 | 0.7685 | |
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| 0.2012 | 16.9984 | 5312 | 0.5489 | 0.7702 | |
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| 0.2198 | 18.0 | 5625 | 0.5560 | 0.7752 | |
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| 0.2171 | 18.9984 | 5937 | 0.5570 | 0.7725 | |
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| 0.2116 | 20.0 | 6250 | 0.5622 | 0.7625 | |
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| 0.2162 | 20.9984 | 6562 | 0.5587 | 0.7668 | |
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| 0.224 | 22.0 | 6875 | 0.5456 | 0.7712 | |
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| 0.212 | 22.9984 | 7187 | 0.5647 | 0.7652 | |
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| 0.2084 | 24.0 | 7500 | 0.5533 | 0.7672 | |
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| 0.2226 | 24.9984 | 7812 | 0.5434 | 0.7705 | |
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| 0.2173 | 26.0 | 8125 | 0.5738 | 0.7675 | |
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| 0.2216 | 26.9984 | 8437 | 0.5557 | 0.7672 | |
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| 0.1918 | 28.0 | 8750 | 0.5502 | 0.7705 | |
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| 0.199 | 28.9984 | 9062 | 0.5456 | 0.7675 | |
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| 0.21 | 29.9520 | 9360 | 0.5483 | 0.7715 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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