# YOLOv5 - Object Detection

YOLOv5 is a widely-used object detection model developed by Ultralytics, known for its balance of speed and accuracy. It remains popular for production deployments.

## Overview

YOLOv5 for object detection features:

- **Mature and stable** - Well-tested in production environments
- **Efficient architecture** - Good balance of speed and accuracy
- **Multiple model sizes** - From nano to extra-large variants
- **Wide adoption** - Extensive community support and resources

## License

**AGPL-3.0**

!!! info "Commercial Licensing"
    - **AGPL-3.0**: Free for open-source projects. Requires derivative works to be open-sourced.
    - **Paid Roboflow customers**: Automatically get access to use any YOLOv5 models trained on or uploaded to the Roboflow platform for commercial use.
    - **Free Roboflow customers**: Can use YOLOv5 via the serverless hosted API, or commercially self-hosted with a paid plan.

    Learn more: [Roboflow Licensing](https://roboflow.com/licensing) | [YOLOv5 License Details](https://roboflow.com/model-licenses/yolov5)

## Pre-trained Model IDs

No pre-trained model aliases are available. Train custom models on Roboflow.

## Supported Backends

| Backend | Extras Required |
|---------|----------------|
| `onnx` | `onnx-cpu`, `onnx-cu12`, `onnx-cu118`, `onnx-jp6-cu126` |

## Roboflow Platform Compatibility

| Feature | Supported |
|---------|-----------|
| **Training** | ❌ Not available for training |
| **Upload Weights** | ✅ Upload pre-trained weights ([guide](https://docs.roboflow.com/deploy/upload-custom-weights)) |
| **Serverless API (v2)** | ✅ [Deploy via hosted API](https://docs.roboflow.com/deploy/serverless-hosted-api-v2) |
| **Workflows** | ✅ Use in [Workflows](https://docs.roboflow.com/workflows) via Object Detection block |
| **Edge Deployment (Jetson)** | ✅ Deploy on NVIDIA Jetson devices |
| **Self-Hosting** | ✅ Deploy with `inference-models` |

**Custom model ID format:** `project-url/version` (e.g., `my-project-abc123/2`)

## Installation

Install with one of the following extras:

- **ONNX**: `onnx-cpu`, `onnx-cu12`

## Usage Example

```python
import cv2
import supervision as sv
from inference_models import AutoModel

# Load model and image
model = AutoModel.from_pretrained("my-project-abc123/2", api_key="your_roboflow_api_key")
image = cv2.imread("path/to/image.jpg")

# Run inference and convert to supervision Detections
predictions = model(image)
detections = predictions[0].to_supervision()

# Annotate image
bounding_box_annotator = sv.BoxAnnotator()
annotated_image = bounding_box_annotator.annotate(image, detections)

# Save or display
cv2.imwrite("annotated.jpg", annotated_image)
```

