README.md

---
title: Farm Object Detection API
emoji: 🔍
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.28.3
app_file: app.py
pinned: false
license: apache-2.0
short_description: Object detection for farm equipment, crops, and workers
---

# 🔍 Farm Object Detection API

High-performance object detection for agricultural environments using RT-DETR models.

## 🎯 Capabilities
- **Farm Equipment Detection**: Tractors, harvesters, tools
- **Crop Counting**: Automated inventory of plants and produce
- **Worker Safety**: Personnel detection and activity monitoring
- **Animal Detection**: Livestock and wildlife identification

## 🤖 Models
- **RT-DETR R18VD**: Lightweight, fast inference (15-30 FPS)
- **RT-DETR R34VD**: Balanced performance and accuracy
- **RT-DETR R50VD**: High accuracy for detailed analysis

## 📡 API Usage

### Python
```python
import requests
import base64

def detect_objects(image_path, model="r50vd"):
    with open(image_path, "rb") as f:
        image_b64 = base64.b64encode(f.read()).decode()
    
    response = requests.post(
        "https://YOUR-USERNAME-farm-object-detection.hf.space/api/predict",
        json={"data": [image_b64, model]}
    )
    return response.json()

result = detect_objects("farm_image.jpg")
print(result)
```

### JavaScript
```javascript
async function detectObjects(imageFile, model = 'r50vd') {
    const base64 = await fileToBase64(imageFile);
    
    const response = await fetch(
        'https://YOUR-USERNAME-farm-object-detection.hf.space/api/predict',
        {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ data: [base64, model] })
        }
    );
    
    return await response.json();
}
```

## 📊 Response Format
```json
{
  "objects_detected": 12,
  "detections": [
    {
      "class": "tractor",
      "confidence": 0.95,
      "bbox": [100, 150, 400, 350],
      "area": 75000
    }
  ],
  "processing_time": 0.8,
  "model_used": "rtdetr_r50vd"
}
```