| language: en | |
| license: mit | |
| tags: | |
| - pytorch | |
| - image-classification | |
| - satellite-imagery | |
| - computer-vision | |
| - eurosat | |
| datasets: | |
| - eurosat | |
| metrics: | |
| - accuracy | |
| pipeline_tag: image-classification | |
| # SimpleNet — EuroSAT Land-Use Classifier | |
| Lightweight CNN (~850K params) trained from scratch on the EuroSAT dataset | |
| for 10-class satellite image classification. | |
| ## Usage | |
| ```python | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| # Download | |
| weights = hf_hub_download(repo_id="yava-code/eurosat-simplenet", filename="best_model.pth") | |
| # Load (you need model.py from this repo) | |
| from model import SimpleNet, CLASS_NAMES | |
| model = SimpleNet() | |
| model.load_state_dict(torch.load(weights, map_location="cpu")) | |
| model.eval() | |
| ``` | |
| ## Architecture | |
| 4 convolutional blocks (Conv→BN→ReLU→Pool) + FC classifier. | |
| Channels: 3→32→64→128→256. Spatial: 64→32→16→8→4. | |
| ## Training | |
| - **Dataset**: EuroSAT (27,000 images, 10 classes) | |
| - **Optimizer**: Adam (lr=1e-3, StepLR γ=0.1 every 5 epochs) | |
| - **Augmentations**: Flip, Rotation ±15°, ColorJitter | |
| - **Epochs**: 20 | |
| ## Demo | |
| 👉 [Try the live demo on Spaces](https://huggingface.co/spaces/yava-code/eurostat) | |