---
library_name: onnx
tags:
  - object-detection
  - yolo
  - yolox
  - yolo9
  - rfdetr
  - onnxruntime-web
  - webgpu
  - browser
  - libreyolo
license: mit
---

# LibreYOLO Web — Model Zoo

Pre-exported ONNX models for [libreyolo-web](https://github.com/xuban-ceccon/libreyolo-web), the browser-based multi-family YOLO detection library.

## Usage

```bash
npm install libreyolo-web onnxruntime-web
```

```typescript
import { loadModel } from 'libreyolo-web';

// Auto-downloads from this repo, zero config
const model = await loadModel('yolox-nano');
const result = await model.predict(imageElement);
```

## Available Models

### YOLOX (letterbox + BGR + 0-255)
| Name | Input | Size | File |
|------|-------|------|------|
| `yolox-nano` | 416 | 3.6MB | `yolox_n.onnx` |
| `yolox-tiny` | 416 | 19MB | `yolox_t.onnx` |
| `yolox-small` | 640 | 34MB | `yolox_s.onnx` |
| `yolox-medium` | 640 | 97MB | `yolox_m.onnx` |
| `yolox-large` | 640 | 207MB | `yolox_l.onnx` |
| `yolox-xlarge` | 640 | 378MB | `yolox_x.onnx` |

### YOLO9 (resize + RGB + /255)
| Name | Input | Size | File |
|------|-------|------|------|
| `yolo9-tiny` | 640 | 8MB | `yolo9_t.onnx` |
| `yolo9-small` | 640 | 28MB | `yolo9_s.onnx` |
| `yolo9-medium` | 640 | 77MB | `yolo9_m.onnx` |
| `yolo9-compact` | 640 | 97MB | `yolo9_c.onnx` |

### RF-DETR (resize + ImageNet norm, no NMS)
| Name | Input | Size | File |
|------|-------|------|------|
| `rfdetr-nano` | 384 | 103MB | `rfdetr_n.onnx` |
| `rfdetr-small` | 512 | 109MB | `rfdetr_s.onnx` |
| `rfdetr-medium` | 576 | 115MB | `rfdetr_m.onnx` |
| `rfdetr-large` | 704 | 116MB | `rfdetr_l.onnx` |

## Training

All models were trained on COCO using [libreyolo](https://github.com/LibreYOLO/libreyolo) (Python) and exported to ONNX with `model.export(format='onnx', simplify=True)`.

## License

MIT