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