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--- |
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library_name: transformers |
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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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- precision |
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- recall |
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- f1 |
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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: 0.9619047619047619 |
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- name: Precision |
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type: precision |
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value: 0.9583926593893372 |
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- name: Recall |
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type: recall |
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value: 0.9619047619047619 |
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- name: F1 |
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type: f1 |
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value: 0.9587351719211175 |
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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: 0.0711 |
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- Accuracy: 0.9619 |
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- Precision: 0.9584 |
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- Recall: 0.9619 |
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- F1: 0.9587 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.2262 | 0.9888 | 22 | 0.2061 | 0.9365 | 0.8770 | 0.9365 | 0.9058 | |
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| 0.1666 | 1.9775 | 44 | 0.1274 | 0.9333 | 0.8769 | 0.9333 | 0.9042 | |
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| 0.1168 | 2.9663 | 66 | 0.1054 | 0.9524 | 0.9461 | 0.9524 | 0.9438 | |
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| 0.0984 | 4.0 | 89 | 0.0824 | 0.9619 | 0.9591 | 0.9619 | 0.9599 | |
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| 0.1028 | 4.9888 | 111 | 0.0814 | 0.9714 | 0.9697 | 0.9714 | 0.9695 | |
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| 0.1082 | 5.9775 | 133 | 0.0835 | 0.9492 | 0.9518 | 0.9492 | 0.9329 | |
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| 0.0962 | 6.9663 | 155 | 0.0872 | 0.9587 | 0.9578 | 0.9587 | 0.9582 | |
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| 0.0799 | 8.0 | 178 | 0.0803 | 0.9587 | 0.9543 | 0.9587 | 0.9546 | |
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| 0.0954 | 8.9888 | 200 | 0.0685 | 0.9619 | 0.9584 | 0.9619 | 0.9587 | |
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| 0.0771 | 9.8876 | 220 | 0.0711 | 0.9619 | 0.9584 | 0.9619 | 0.9587 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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