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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/swinv2-tiny-patch4-window8-256 |
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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: swinv2-tiny-patch4-window8-256-Ocular-Toxoplasmosis |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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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: 0.8387096774193549 |
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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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# swinv2-tiny-patch4-window8-256-Ocular-Toxoplasmosis |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co./microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5167 |
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- Accuracy: 0.8387 |
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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: 40 |
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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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| No log | 0.7273 | 2 | 1.4057 | 0.2419 | |
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| No log | 1.8182 | 5 | 1.2100 | 0.4677 | |
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| No log | 2.9091 | 8 | 1.1808 | 0.4516 | |
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| 1.3062 | 4.0 | 11 | 1.0975 | 0.5968 | |
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| 1.3062 | 4.7273 | 13 | 1.0542 | 0.6613 | |
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| 1.3062 | 5.8182 | 16 | 0.9857 | 0.6613 | |
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| 1.3062 | 6.9091 | 19 | 0.9176 | 0.6774 | |
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| 1.0003 | 8.0 | 22 | 0.8761 | 0.6774 | |
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| 1.0003 | 8.7273 | 24 | 0.8540 | 0.6774 | |
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| 1.0003 | 9.8182 | 27 | 0.7777 | 0.6613 | |
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| 0.8096 | 10.9091 | 30 | 0.7498 | 0.6613 | |
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| 0.8096 | 12.0 | 33 | 0.7569 | 0.6613 | |
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| 0.8096 | 12.7273 | 35 | 0.7422 | 0.6774 | |
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| 0.8096 | 13.8182 | 38 | 0.7278 | 0.7097 | |
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| 0.6556 | 14.9091 | 41 | 0.6877 | 0.7258 | |
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| 0.6556 | 16.0 | 44 | 0.6433 | 0.7258 | |
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| 0.6556 | 16.7273 | 46 | 0.6324 | 0.7419 | |
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| 0.6556 | 17.8182 | 49 | 0.6390 | 0.7419 | |
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| 0.5725 | 18.9091 | 52 | 0.6504 | 0.7742 | |
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| 0.5725 | 20.0 | 55 | 0.6145 | 0.7581 | |
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| 0.5725 | 20.7273 | 57 | 0.5824 | 0.7903 | |
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| 0.5057 | 21.8182 | 60 | 0.5476 | 0.8226 | |
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| 0.5057 | 22.9091 | 63 | 0.5413 | 0.8226 | |
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| 0.5057 | 24.0 | 66 | 0.5335 | 0.8226 | |
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| 0.5057 | 24.7273 | 68 | 0.5302 | 0.8226 | |
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| 0.4945 | 25.8182 | 71 | 0.5231 | 0.8226 | |
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| 0.4945 | 26.9091 | 74 | 0.5167 | 0.8387 | |
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| 0.4945 | 28.0 | 77 | 0.5132 | 0.8387 | |
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| 0.4945 | 28.7273 | 79 | 0.5131 | 0.8387 | |
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| 0.4883 | 29.0909 | 80 | 0.5131 | 0.8387 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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