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--- |
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license: mit |
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tags: |
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- image-classification |
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- remote-sensing |
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- resnet |
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- pytorch |
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- transformers |
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--- |
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# RSP-ResNet-50 |
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ResNet-50 based model for remote sensing scene classification (51 classes). |
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## Usage |
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```python |
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from transformers import AutoModelForImageClassification |
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import torch |
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# Load model |
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model = AutoModelForImageClassification.from_pretrained( |
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"BiliSakura/RSP-ResNet-50", |
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trust_remote_code=True |
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) |
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# Inference |
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model.eval() |
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input_image = torch.randn(1, 3, 224, 224) # (batch, channels, height, width) |
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with torch.no_grad(): |
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outputs = model(pixel_values=input_image) |
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logits = outputs.logits # Shape: (1, 51) |
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predicted_class = logits.argmax(dim=-1).item() |
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``` |
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## Model Details |
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- **Architecture:** ResNet-50 |
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- **Input size:** 224×224×3 |
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- **Number of classes:** 51 |
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- **Parameters:** ~23.6M |
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## Citation |
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If you use this model, please cite the original RSP paper: |
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```bibtex |
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@ARTICLE{rsp, |
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author={Wang, Di and Zhang, Jing and Du, Bo and Xia, Gui-Song and Tao, Dacheng}, |
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journal={IEEE Transactions on Geoscience and Remote Sensing}, |
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title={An Empirical Study of Remote Sensing Pretraining}, |
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year={2023}, |
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volume={61}, |
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number={}, |
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pages={1-20}, |
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doi={10.1109/TGRS.2022.3176603} |
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} |
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``` |
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**Original Repository:** [ViTAE-Transformer/RSP](https://github.com/ViTAE-Transformer/RSP) |
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