demon2233/rtmlib-ts

Models

This directory contains pre-trained ONNX models for object detection and pose estimation.

Directory Structure

models/
├── yolo/
│   ├── yolov12n.onnx      # YOLO12 Nano (object detection)
│   └── yolov26n.onnx      # YOLO12 Nano (alternative)
├── rtmpose/
│   └── end2end.onnx       # RTMW (pose estimation)
└── README.md

Available Models

Object Detection

Model	File	Size	Classes	Input	Description
YOLO12n	yolo/yolov12n.onnx	~11 MB	80 COCO	640×640	YOLO12 Nano - fastest
YOLOv26n	yolo/yolov26n.onnx	~11 MB	80 COCO	640×640	YOLO12 Nano variant - balanced

Model Selection:

• YOLO12n: Fastest inference, best for real-time applications
• YOLv26n: Slightly different architecture, may have better accuracy on some objects

Pose Estimation

Model	File	Size	Keypoints	Input	Description
RTMW	rtmpose/end2end.onnx	~50 MB	17 COCO	384×288	RTMW wholebody

Usage

ObjectDetector

import { ObjectDetector } from 'rtmlib-ts';

const detector = new ObjectDetector({
  model: 'models/yolo/yolov12n.onnx',
  classes: ['person', 'car'],  // Filter classes or null for all
});
await detector.init();

const objects = await detector.detectFromCanvas(canvas);

PoseDetector

import { PoseDetector } from 'rtmlib-ts';

const detector = new PoseDetector({
  detModel: 'models/yolo/yolov12n.onnx',
  poseModel: 'models/rtmpose/end2end.onnx',
});
await detector.init();

const people = await detector.detectFromCanvas(canvas);

Model Paths (Relative)

When using the library, reference models with relative paths from your web root:

// From web root (rtmlib-ts/)
const detector = new ObjectDetector({
  model: './models/yolo/yolov12n.onnx',
});

// From examples/
const detector = new ObjectDetector({
  model: '../models/yolo/yolov12n.onnx',
});

Performance

YOLO12n (Object Detection)

Backend	Input Size	Inference Time
WASM	640×640	~80ms
WASM	416×416	~40ms
WebGPU	640×640	~30ms

RTMW (Pose Estimation)

Backend	Input Size	Inference Time (per person)
WASM	384×288	~25ms
WebGPU	384×288	~10ms

COCO Classes (80)

YOLO12n detects all 80 COCO classes:

0: person         20: elephant      40: cup           60: toilet
1: bicycle        21: bear          41: fork          61: tv
2: car            22: zebra         42: knife         62: laptop
3: motorcycle     23: giraffe       43: spoon         63: mouse
4: airplane       24: backpack      44: bowl          64: remote
5: bus            25: umbrella      45: banana        65: keyboard
6: train          26: handbag       46: apple         66: cell phone
7: truck          27: tie           47: sandwich      67: microwave
8: boat           28: suitcase      48: orange        68: oven
9: traffic light  29: frisbee       49: broccoli      69: toaster
10: fire hydrant  30: skis          50: carrot        70: sink
11: stop sign     31: snowboard     51: hot dog       71: refrigerator
12: parking meter 32: sports ball   52: pizza         72: book
13: bench         33: kite          53: donut         73: clock
14: bird          34: baseball bat  54: cake          74: vase
15: cat           35: baseball glove 55: chair        75: scissors
16: dog           36: skateboard    56: couch         76: teddy bear
17: horse         37: surfboard     57: potted plant  77: hair drier
18: sheep         38: tennis racket 58: bed           78: toothbrush
19: cow           39: bottle        59: dining table

License

• YOLO12: AGPL-3.0 (Ultralytics)
• RTMW: Apache-2.0 (OpenMMLab)