This is an official implementation of:
Xuanhan Wang, Lianli Gao, Yan Dai, Yixuan Zhou and Jingkuan Song. Semantic-aware Transfer with Instance-adaptive Parsing for Crowded Scenes Pose Estimation
This repo is built on deep-high-resolution-net.
Method | Backbone | Input size | AP | Ap .5 | AP .75 | AP (Easy) | AP (Medium) | AP (Hard) |
---|---|---|---|---|---|---|---|---|
HRNet | HRNet-w32 | 256 x 192 | 71.7 | 89.8 | 76.9 | 79.6 | 72.7 | 61.5 |
HRNet + STIP | HRNet-w32 | 256 x 192 | 74.1 | 90.0 | 79.9 | 81.6 | 75.1 | 64.3 |
HRNet | HRNet-w48 | 256 x 192 | 73.3 | 90.0 | 78.7 | 81.0 | 74.4 | 63.4 |
HRNet + STIP | HRNet-w48 | 256 x 192 | 74.8 | 90.8 | 80.1 | 82.0 | 75.7 | 65.0 |
The environment can be referred to README.md.
The details about dataset can be referred to README.md.
Downlaod pretrained weights from BaidunYun(Password: wp30) to ./models.
Testing HRNet
python tools/script_test.py \
--cfg experiments/crowdpose/hrnet/w32_256x192_adam_lr1e-3.yaml \
TEST.MODEL_FILE models/pytorch/crowdpose/hrnet_w32_256x192.pth
Testing STIP net
python tools/script_test.py \
--cfg experiments/crowdpose/partnet/stipnet_w32_256x192_adam_lr1e-3.yaml \
TEST.MODEL_FILE models/pytorch/crowdpose/stipnet_w32_256x192.pth
python tools/script_train.py \
--cfg experiments/crowdpose/partnet/stipnet_w32_256x192_adam_lr1e-3.yaml