Shot Doctor models
The pose models Shot Doctor runs in the browser to analyse basketball jump shots, hosted here so the app can load them from a CDN, pinned to a commit.
| File | Model | Changes | Source | Licence |
|---|---|---|---|---|
rtmw-m.onnx | RTMW-m whole-body pose: 133 keypoints (body, feet, face, hands), 256 × 192 input | Weights converted to fp16 (inputs and outputs stay fp32) | OpenMMLab RTMW, the rtmw-dw-l-m_simcc-cocktail14_270e-256x192 ONNX from rtmlib | Apache-2.0 |
pose_landmarker_full.task | MediaPipe Pose Landmarker (full) | None | Google MediaPipe | Apache-2.0 |
RTMW-m
- Input
input: float32[N, 3, 256, 192], RGB, normalised with mean(123.675, 116.28, 103.53)and std(58.395, 57.12, 57.375); the person's box padded 1.25× and widened or heightened to 3 : 4. - Outputs
simcc_x[N, 133, 384]andsimcc_y[N, 133, 512]: the argmax of each over 2 gives the keypoint in input pixels, and the mean of the two maxima its score (COCO-WholeBody keypoint order). - Converted with:
import onnx
from onnxconverter_common import float16
m = onnx.load('rtmw-dw-l-m_simcc-cocktail14_270e-256x192_20231122.onnx')
onnx.save(float16.convert_float_to_float16(m, keep_io_types=True), 'rtmw-m.onnx')
Runs on WebGPU in ONNX Runtime Web 1.30.