---
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)