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--- |
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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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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: swin-tiny-patch4-window7-224-finetuned-wuhan |
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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: 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: 1.0 |
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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-wuhan |
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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 the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Accuracy: 1.0 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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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: 50 |
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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 | 1.0 | 3 | 0.6245 | 0.7778 | |
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| No log | 2.0 | 6 | 0.5321 | 0.7778 | |
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| No log | 3.0 | 9 | 0.5123 | 0.7778 | |
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| 0.6482 | 4.0 | 12 | 0.4956 | 0.7778 | |
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| 0.6482 | 5.0 | 15 | 0.4585 | 0.7778 | |
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| 0.6482 | 6.0 | 18 | 0.3743 | 0.8611 | |
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| 0.5574 | 7.0 | 21 | 0.2842 | 0.9167 | |
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| 0.5574 | 8.0 | 24 | 0.2125 | 0.9167 | |
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| 0.5574 | 9.0 | 27 | 0.2683 | 0.9167 | |
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| 0.4882 | 10.0 | 30 | 0.1316 | 0.9444 | |
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| 0.4882 | 11.0 | 33 | 0.1366 | 0.9444 | |
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| 0.4882 | 12.0 | 36 | 0.0745 | 0.9722 | |
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| 0.4882 | 13.0 | 39 | 0.1065 | 0.9444 | |
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| 0.0907 | 14.0 | 42 | 0.0477 | 0.9722 | |
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| 0.0907 | 15.0 | 45 | 0.0460 | 0.9444 | |
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| 0.0907 | 16.0 | 48 | 0.0438 | 0.9722 | |
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| 0.0481 | 17.0 | 51 | 0.0203 | 1.0 | |
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| 0.0481 | 18.0 | 54 | 0.0093 | 1.0 | |
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| 0.0481 | 19.0 | 57 | 0.0082 | 1.0 | |
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| 0.013 | 20.0 | 60 | 0.0017 | 1.0 | |
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| 0.013 | 21.0 | 63 | 0.0008 | 1.0 | |
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| 0.013 | 22.0 | 66 | 0.0002 | 1.0 | |
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| 0.013 | 23.0 | 69 | 0.0001 | 1.0 | |
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| 0.0101 | 24.0 | 72 | 0.0938 | 0.9722 | |
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| 0.0101 | 25.0 | 75 | 0.1019 | 0.9722 | |
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| 0.0101 | 26.0 | 78 | 0.0005 | 1.0 | |
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| 0.0085 | 27.0 | 81 | 0.0000 | 1.0 | |
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| 0.0085 | 28.0 | 84 | 0.0000 | 1.0 | |
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| 0.0085 | 29.0 | 87 | 0.0001 | 1.0 | |
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| 0.0196 | 30.0 | 90 | 0.0001 | 1.0 | |
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| 0.0196 | 31.0 | 93 | 0.0001 | 1.0 | |
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| 0.0196 | 32.0 | 96 | 0.0000 | 1.0 | |
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| 0.0196 | 33.0 | 99 | 0.0000 | 1.0 | |
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| 0.0027 | 34.0 | 102 | 0.0000 | 1.0 | |
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| 0.0027 | 35.0 | 105 | 0.0000 | 1.0 | |
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| 0.0027 | 36.0 | 108 | 0.0000 | 1.0 | |
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| 0.0016 | 37.0 | 111 | 0.0000 | 1.0 | |
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| 0.0016 | 38.0 | 114 | 0.0000 | 1.0 | |
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| 0.0016 | 39.0 | 117 | 0.0000 | 1.0 | |
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| 0.0021 | 40.0 | 120 | 0.0000 | 1.0 | |
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| 0.0021 | 41.0 | 123 | 0.0000 | 1.0 | |
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| 0.0021 | 42.0 | 126 | 0.0000 | 1.0 | |
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| 0.0021 | 43.0 | 129 | 0.0000 | 1.0 | |
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| 0.0024 | 44.0 | 132 | 0.0000 | 1.0 | |
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| 0.0024 | 45.0 | 135 | 0.0000 | 1.0 | |
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| 0.0024 | 46.0 | 138 | 0.0000 | 1.0 | |
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| 0.0009 | 47.0 | 141 | 0.0000 | 1.0 | |
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| 0.0009 | 48.0 | 144 | 0.0000 | 1.0 | |
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| 0.0009 | 49.0 | 147 | 0.0000 | 1.0 | |
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| 0.0006 | 50.0 | 150 | 0.0000 | 1.0 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.1 |
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- Tokenizers 0.13.3 |
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