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--- |
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license: apache-2.0 |
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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-eurosat |
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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: 0.7302889760970389 |
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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-eurosat |
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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: 0.5574 |
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- Accuracy: 0.7303 |
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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: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 1024 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6271 | 0.99 | 98 | 0.6035 | 0.6926 | |
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| 0.6156 | 1.99 | 197 | 0.5844 | 0.7006 | |
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| 0.6148 | 3.0 | 296 | 0.5758 | 0.7104 | |
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| 0.6055 | 4.0 | 395 | 0.5853 | 0.7015 | |
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| 0.5938 | 4.99 | 493 | 0.5858 | 0.7104 | |
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| 0.5878 | 5.99 | 592 | 0.5630 | 0.7210 | |
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| 0.5873 | 7.0 | 691 | 0.5620 | 0.7236 | |
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| 0.5947 | 8.0 | 790 | 0.5670 | 0.7196 | |
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| 0.5866 | 8.99 | 888 | 0.5592 | 0.7265 | |
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| 0.5807 | 9.99 | 987 | 0.5574 | 0.7254 | |
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| 0.5764 | 11.0 | 1086 | 0.5655 | 0.7245 | |
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| 0.5729 | 12.0 | 1185 | 0.5611 | 0.7237 | |
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| 0.577 | 12.99 | 1283 | 0.5702 | 0.7189 | |
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| 0.5702 | 13.99 | 1382 | 0.5588 | 0.7259 | |
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| 0.5717 | 15.0 | 1481 | 0.5565 | 0.7244 | |
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| 0.5646 | 16.0 | 1580 | 0.5536 | 0.7303 | |
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| 0.5591 | 16.99 | 1678 | 0.5525 | 0.7345 | |
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| 0.5586 | 17.99 | 1777 | 0.5565 | 0.7286 | |
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| 0.5668 | 19.0 | 1876 | 0.5520 | 0.7304 | |
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| 0.5617 | 20.0 | 1975 | 0.5557 | 0.7289 | |
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| 0.5546 | 20.99 | 2073 | 0.5561 | 0.7325 | |
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| 0.5579 | 21.99 | 2172 | 0.5537 | 0.7314 | |
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| 0.5604 | 23.0 | 2271 | 0.5545 | 0.7290 | |
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| 0.5563 | 24.0 | 2370 | 0.5591 | 0.7288 | |
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| 0.5634 | 24.99 | 2468 | 0.5546 | 0.7307 | |
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| 0.5563 | 25.99 | 2567 | 0.5557 | 0.7303 | |
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| 0.5563 | 27.0 | 2666 | 0.5571 | 0.7276 | |
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| 0.5544 | 28.0 | 2765 | 0.5551 | 0.7298 | |
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| 0.5491 | 28.99 | 2863 | 0.5596 | 0.7282 | |
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| 0.5461 | 29.77 | 2940 | 0.5574 | 0.7303 | |
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### Framework versions |
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- Transformers 4.29.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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