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
1from huggingface_hub import hf_hub_download
2import torch
3from torchvision import transforms
4from PIL import Image
5
6checkpoint = torch.load(
7 hf_hub_download("Sathya77/swin-transformer-satellite", "swin_resisc45.pth"),
8 map_location='cpu'
9)
10
11model = SwinTransformer(embed_dim=96, num_classes=45)
12model.load_state_dict(checkpoint['model_state_dict'])
13model.eval()
Live Demo
References
