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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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- image_folder |
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- nielsr/eurosat-demo |
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metrics: |
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- accuracy |
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widget: |
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- src: https://drive.google.com/uc?id=1trKgvkMRQ3BB0VcqnDwmieLxXhWmS8rq |
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example_title: Annual Crop |
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- src: https://drive.google.com/uc?id=1kWQbPNHVa_JscS0age5E0UOSBcU1bh18 |
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example_title: Forest |
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- src: https://drive.google.com/uc?id=12YbxF-MfpMqLPB91HuTPEgcg1xnZKhGP |
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example_title: Herbaceous Vegetation |
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- src: https://drive.google.com/uc?id=1NkzDiaQ1ciMDf89C8uA5zGx984bwkFCi |
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example_title: Highway |
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- src: https://drive.google.com/uc?id=1F6r7O0rlgzaPvY6XBpFOWUTIddEIUkxx |
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example_title: Industrial |
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- src: https://drive.google.com/uc?id=16zOtFHZ9E17jA9Ua4PsXrUjugSs77XKm |
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example_title: Pasture |
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- src: https://drive.google.com/uc?id=163tqIdoVY7WFtKQlpz_bPM9WjwbJAtd |
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example_title: Permanent Crop |
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- src: https://drive.google.com/uc?id=1qsX-XsrE3dMp7C7LLVa6HriaABIXuBrJ |
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example_title: Residential |
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- src: https://drive.google.com/uc?id=1UK2praQHbNXDnctJt58rrlQZu84lxyk |
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example_title: River |
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- src: https://drive.google.com/uc?id=1zVAfR7N5hXy6eq1cVOd8bXPjC1sqxVir |
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example_title: Sea Lake |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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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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type: image-classification |
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name: Image Classification |
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dataset: |
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name: image_folder |
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type: image_folder |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9848148148148148 |
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name: Accuracy |
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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 image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0536 |
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- Accuracy: 0.9848 |
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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: 5 |
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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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| 0.2602 | 1.0 | 190 | 0.1310 | 0.9563 | |
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| 0.1975 | 2.0 | 380 | 0.1063 | 0.9637 | |
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| 0.142 | 3.0 | 570 | 0.0642 | 0.9767 | |
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| 0.1235 | 4.0 | 760 | 0.0560 | 0.9837 | |
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| 0.1019 | 5.0 | 950 | 0.0536 | 0.9848 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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