---
title: Car Detection
emoji: 🐢
colorFrom: gray
colorTo: yellow
sdk: docker
pinned: false
license: cc-by-nc-nd-4.0
short_description: car-counting
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Parking - Aerial Photo Car Detection

Detect and count cars in aerial/satellite photography using an RF-DETR object detection model, served via a FastAPI backend with a Preact frontend.

## Architecture

```
training/     Model training, export, and inference scripts (RF-DETR + ONNX)
server/       FastAPI backend — serves ONNX model predictions
frontend/     Preact + Vite + TypeScript UI
```

## Quick Start

### Prerequisites

- Python 3.11+
- Node.js 18+
- Trained ONNX model at `training/exported_models/inference_model.sim.onnx`

### Backend

```bash
pip install -r server/requirements.txt
uvicorn server.main:app --reload --port 8000
```

### Frontend

```bash
cd frontend
npm install
npm run dev
```

Open http://localhost:5173, upload an aerial image, and view detection results.

## API

### `POST /detect`

Multipart form upload. Returns detected cars with annotated and heatmap images.

| Parameter   | Type  | Default | Description              |
|-------------|-------|---------|--------------------------|
| `file`      | file  | —       | JPEG or PNG image        |
| `threshold` | float | 0.5     | Confidence threshold 0–1 |

Response:

```json
{
  "car_count": 47,
  "detections": [{"bbox": [x1, y1, x2, y2], "score": 0.97}, ...],
  "annotated_image": "data:image/jpeg;base64,...",
  "heatmap_image": "data:image/jpeg;base64,..."
}
```

### `GET /health`

Returns `{"status": "ok", "model_loaded": true}`.

## Deployment

The backend and frontend are deployed separately so the page loads instantly (static CDN) while the model runs server-side.

### Backend — Hugging Face Spaces

1. Create a new Space at [huggingface.co/new-space](https://huggingface.co/new-space) with **Docker** SDK
2. Clone the Space repo and copy in the required files:
   ```bash
   git clone https://huggingface.co/spaces/YOUR_USER/YOUR_SPACE
   cd YOUR_SPACE
   cp /path/to/parking/Dockerfile .
   cp -r /path/to/parking/server ./server
   mkdir -p training/exported_models
   cp /path/to/parking/training/exported_models/inference_model.sim.onnx ./training/exported_models/
   git add . && git commit -m "Initial deploy" && git push
   ```
3. The Space will build the Docker image and start serving at `https://YOUR_USER-YOUR_SPACE.hf.space`

### Frontend — Vercel

1. Push the `frontend/` directory to a GitHub repo (or use the full project repo)
2. Import the project at [vercel.com/new](https://vercel.com/new)
3. Set **Root Directory** to `frontend`
4. Add the environment variable:
   - `VITE_API_URL` = `https://YOUR_USER-YOUR_SPACE.hf.space`
5. Deploy — Vercel auto-detects Vite and builds it

## Model

- Architecture: RF-DETR (medium)
- Single class: car
- Trained on aerial parking lot imagery in YOLO format
- Exported to ONNX with graph simplification
- Training scripts and data live under `training/`