Create README.md

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- swin-transformer
+- satellite-imagery
+- image-classification
+- pytorch
+- computer-vision
+pipeline_tag: image-classification
+---
+
+# Swin Transformer — Satellite Image Classification
+
+PyTorch implementation of Swin Transformer (Liu et al. 2021) trained on NWPU-RESISC45 satellite imagery dataset.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Architecture | Swin Transformer (4 stages) |
+| Dataset | NWPU-RESISC45 |
+| Classes | 45 land use categories |
+| Test Accuracy | 82% |
+| Input Size | 224×224 |
+| Embed Dim | 96 |
+| Training Hardware | RTX 4050 6GB |
+| Framework | PyTorch (from scratch) |
+
+## Classes
+airplane, airport, baseball_diamond, basketball_court, beach, bridge,
+chaparral, church, circular_farmland, cloud, commercial_area, dense_residential,
+desert, forest, freeway, golf_course, ground_track_field, harbor, industrial_area,
+intersection, island, lake, meadow, medium_residential, mobile_home_park,
+mountain, overpass, palace, parking_lot, railway, railway_station,
+rectangular_farmland, river, roundabout, runway, sea_ice, ship, snowberg,
+sparse_residential, stadium, storage_tank, tennis_court, terrace, thermal_power_station, wetland
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from torchvision import transforms
+from PIL import Image
+
+checkpoint = torch.load(
+    hf_hub_download("Sathya77/swin-transformer-satellite", "swin_resisc45.pth"),
+    map_location='cpu'
+)
+
+model = SwinTransformer(embed_dim=96, num_classes=45)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+```
+
+## Live Demo
+Try it here: [Sathya77/swin-transformer-satellite](https://huggingface.co/spaces/Sathya77/swin-transformer-satellite)
+
+## References
+- [Swin Transformer Paper](https://arxiv.org/abs/2103.14030) — Liu et al. 2021
+- [NWPU-RESISC45 Dataset](http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html)