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 (Python) and exported to ONNX with model.export(format='onnx', simplify=True).

License

MIT

LibreYOLO/libreyolo-web

LibreYOLO Web — Model Zoo

Usage

Available Models

YOLOX (letterbox + BGR + 0-255)

YOLO9 (resize + RGB + /255)

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

Training

License

LibreYOLO/
libreyolo-web