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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: Electrcical-IMAGE-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: 0.8960396039603961 |
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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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# Electrcical-IMAGE-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: 0.3583 |
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- Accuracy: 0.8960 |
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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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| 1.6143 | 0.98 | 28 | 1.2882 | 0.5347 | |
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| 0.8597 | 2.0 | 57 | 0.7302 | 0.7649 | |
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| 0.5858 | 2.98 | 85 | 0.4849 | 0.8465 | |
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| 0.4332 | 4.0 | 114 | 0.4274 | 0.8614 | |
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| 0.4054 | 4.98 | 142 | 0.3687 | 0.8787 | |
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| 0.3826 | 6.0 | 171 | 0.3788 | 0.8614 | |
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| 0.3561 | 6.98 | 199 | 0.3700 | 0.8936 | |
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| 0.2838 | 8.0 | 228 | 0.3550 | 0.8812 | |
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| 0.2897 | 8.98 | 256 | 0.3698 | 0.8886 | |
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| 0.2519 | 10.0 | 285 | 0.3459 | 0.8837 | |
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| 0.2194 | 10.98 | 313 | 0.3583 | 0.8960 | |
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| 0.1955 | 12.0 | 342 | 0.3442 | 0.8886 | |
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| 0.2443 | 12.98 | 370 | 0.3801 | 0.8787 | |
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| 0.207 | 14.0 | 399 | 0.3499 | 0.8861 | |
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| 0.2078 | 14.98 | 427 | 0.3701 | 0.8837 | |
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| 0.1873 | 16.0 | 456 | 0.3773 | 0.8861 | |
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| 0.1697 | 16.98 | 484 | 0.3753 | 0.8861 | |
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| 0.1812 | 18.0 | 513 | 0.3747 | 0.8911 | |
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| 0.151 | 18.98 | 541 | 0.3736 | 0.8861 | |
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| 0.1567 | 19.65 | 560 | 0.3726 | 0.8861 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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