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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-vit0 |
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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: 0.8314176245210728 |
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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-vit0 |
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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: 0.4836 |
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- Accuracy: 0.8314 |
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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: 20 |
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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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| 1.13 | 0.97 | 18 | 1.0297 | 0.4330 | |
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| 0.9066 | 2.0 | 37 | 0.8349 | 0.6590 | |
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| 0.7157 | 2.97 | 55 | 0.8050 | 0.6743 | |
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| 0.6446 | 4.0 | 74 | 0.6934 | 0.7165 | |
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| 0.5707 | 4.97 | 92 | 0.6324 | 0.7433 | |
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| 0.5042 | 6.0 | 111 | 0.6156 | 0.7356 | |
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| 0.4714 | 6.97 | 129 | 0.6825 | 0.7241 | |
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| 0.4225 | 8.0 | 148 | 0.5692 | 0.7625 | |
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| 0.3912 | 8.97 | 166 | 0.6150 | 0.7586 | |
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| 0.3442 | 10.0 | 185 | 0.4901 | 0.8008 | |
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| 0.289 | 10.97 | 203 | 0.5580 | 0.7739 | |
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| 0.2827 | 12.0 | 222 | 0.5308 | 0.7969 | |
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| 0.2375 | 12.97 | 240 | 0.5274 | 0.8046 | |
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| 0.2493 | 14.0 | 259 | 0.5433 | 0.8046 | |
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| 0.2309 | 14.97 | 277 | 0.5355 | 0.7931 | |
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| 0.1963 | 16.0 | 296 | 0.4836 | 0.8314 | |
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| 0.2162 | 16.97 | 314 | 0.4973 | 0.8238 | |
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| 0.2256 | 18.0 | 333 | 0.4918 | 0.8276 | |
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| 0.2124 | 18.97 | 351 | 0.5071 | 0.8161 | |
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| 0.1797 | 19.46 | 360 | 0.4985 | 0.8199 | |
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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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