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README.md

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+---
+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)