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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-herbify |
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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-herbify |
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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: 0.0378 |
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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: 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: 35 |
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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 | 0.94 | 4 | 1.8723 | 0.2787 | |
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| No log | 1.88 | 8 | 1.5899 | 0.6885 | |
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| 1.8465 | 2.82 | 12 | 1.1661 | 0.8197 | |
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| 1.8465 | 4.0 | 17 | 0.5156 | 0.9508 | |
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| 0.9675 | 4.94 | 21 | 0.2177 | 0.9836 | |
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| 0.9675 | 5.88 | 25 | 0.0929 | 0.9836 | |
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| 0.9675 | 6.82 | 29 | 0.0378 | 1.0 | |
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| 0.2342 | 8.0 | 34 | 0.0128 | 1.0 | |
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| 0.2342 | 8.94 | 38 | 0.0075 | 1.0 | |
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| 0.1022 | 9.88 | 42 | 0.0053 | 1.0 | |
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| 0.1022 | 10.82 | 46 | 0.0049 | 1.0 | |
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| 0.0553 | 12.0 | 51 | 0.0032 | 1.0 | |
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| 0.0553 | 12.94 | 55 | 0.0022 | 1.0 | |
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| 0.0553 | 13.88 | 59 | 0.0017 | 1.0 | |
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| 0.0278 | 14.82 | 63 | 0.0018 | 1.0 | |
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| 0.0278 | 16.0 | 68 | 0.0012 | 1.0 | |
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| 0.0266 | 16.94 | 72 | 0.0011 | 1.0 | |
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| 0.0266 | 17.88 | 76 | 0.0006 | 1.0 | |
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| 0.046 | 18.82 | 80 | 0.0007 | 1.0 | |
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| 0.046 | 20.0 | 85 | 0.0007 | 1.0 | |
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| 0.046 | 20.94 | 89 | 0.0012 | 1.0 | |
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| 0.0245 | 21.88 | 93 | 0.0015 | 1.0 | |
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| 0.0245 | 22.82 | 97 | 0.0011 | 1.0 | |
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| 0.0249 | 24.0 | 102 | 0.0007 | 1.0 | |
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| 0.0249 | 24.94 | 106 | 0.0006 | 1.0 | |
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| 0.0201 | 25.88 | 110 | 0.0005 | 1.0 | |
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| 0.0201 | 26.82 | 114 | 0.0005 | 1.0 | |
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| 0.0201 | 28.0 | 119 | 0.0004 | 1.0 | |
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| 0.0208 | 28.94 | 123 | 0.0004 | 1.0 | |
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| 0.0208 | 29.88 | 127 | 0.0004 | 1.0 | |
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| 0.0122 | 30.82 | 131 | 0.0004 | 1.0 | |
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| 0.0122 | 32.0 | 136 | 0.0004 | 1.0 | |
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| 0.0222 | 32.94 | 140 | 0.0004 | 1.0 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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