license: mit
library_name: libreyolo
tags:
- keypoint-detection
- pose-estimation
- hrnet
LibreHRNetw32-pose
Official HRNet-W32 256x192 COCO-17 top-down pose weights, repackaged for LibreYOLO.
This is a person-crop pose head. Its fixed input is one 256x192 RGB person
crop; the checkpoint does not contain a person detector. LibreYOLO can compose
the head with its default YOLO9 detector, another PersonDetector, explicit
person_boxes, or cropped=True.
Source
Derived from
leoxiaobin/deep-high-resolution-net.pytorch
at commit 6f69e4676ad8d43d0d61b64b1b9726f0c369e7b1.
The upstream repository license is Copyright (c) 2019 Leo Xiao and is MIT.
Adapted implementation files also identify Microsoft and Bin Xiao.
Official source file: pose_hrnet_w32_256x192.pth
Source SHA-256:
19bc083708bb8d873211e50d85d56344c10290c6e8b564c813fdde09645c4c1c
Modifications
The original state dict is wrapped with LibreYOLO checkpoint metadata. Learned
tensor keys, values, and dtypes are unchanged and strict-load into the native
HRNet-W32 graph. The converted file is LibreHRNetw32-pose.pt, SHA-256
c9d8eb383e63ce795f87a3ee2f2b0c8aa0df9bae981573ec4b3909d63815a825.
See weights/convert_hrnet_weights.py and docs/provenance/hrnet.md in the
LibreYOLO source repository.
License
The official project distributes this checkpoint from its MIT-licensed model
zoo but does not attach a separate checkpoint-specific license. This mirror's
redistribution basis is the MIT license implied by the releasing project, not
a claim of an independently confirmed per-file grant. Training-data rights
remain separate. See LICENSE and NOTICE.