Libreyolo Web

Available Models

YOLOX (letterbox + BGR + 0-255)

YOLO9 (resize + RGB + /255)

RF-DETR (resize + ImageNet norm, no NMS)

Training

All models were trained on COCO using libreyolo (Python) and exported to ONNX with model.export(format='onnx', simplify=True).

License

MIT

onnx · 103 MB · SHA-256 045ab8cc3ea1…75f1 · Hugging Face

--- 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