This is an official pytorch implementation of Simple and Lightweight Human Pose Estimation. The codes are developed based on the repository of HRNet.
| Method | #Params | FLOPs | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean | Mean@0.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| pose_resnet_501 | 34.0M | 12.0G | 96.4 | 95.3 | 89.0 | 83.2 | 88.4 | 84.0 | 79.6 | 88.5 | 34.0 |
| lpn_50 | 2.9M | 1.3G | 96.56 | 95.33 | 88.51 | 83.50 | 88.84 | 84.00 | 79.81 | 88.64 | 34.12 |
- Flip test is used.
- Input size is 256x256.
| Method | #Params | FLOPs | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pose_resnet_501 | 34.0M | 8.9G | 0.704 | 0.886 | 0.783 | 0.671 | 0.772 | 0.763 | 0.929 | 0.834 | 0.721 | 0.824 |
| pose_resnet_1011 | 53.0M | 12.4G | 0.714 | 0.893 | 0.793 | 0.681 | 0.781 | 0.771 | 0.934 | 0.840 | 0.730 | 0.832 |
| pose_resnet_1521 | 68.6M | 15.7G | 0.720 | 0.893 | 0.798 | 0.687 | 0.789 | 0.778 | 0.934 | 0.846 | 0.736 | 0.839 |
| pose_hrnet_w322 | 28.5M | 7.1G | 0.744 | 0.905 | 0.819 | 0.708 | 0.810 | 0.798 | 0.942 | 0.865 | 0.757 | 0.858 |
| pose_hrnet_w482 | 63.6M | 14.6G | 0.751 | 0.906 | 0.822 | 0.715 | 0.818 | 0.804 | 0.943 | 0.867 | 0.762 | 0.864 |
| lpn_50 | 2.9M | 1.0G | 0.691 | 0.881 | 0.766 | 0.659 | 0.757 | 0.749 | 0.923 | 0.818 | 0.707 | 0.810 |
| lpn_101 | 5.3M | 1.4G | 0.704 | 0.886 | 0.781 | 0.672 | 0.772 | 0.762 | 0.929 | 0.831 | 0.721 | 0.822 |
| lpn_152 | 7.4M | 1.8G | 0.710 | 0.892 | 0.786 | 0.678 | 0.777 | 0.768 | 0.933 | 0.834 | 0.726 | 0.827 |
- Flip test is used.
- Input size is 256x192.
- Flip test is used when testing the inference speed.
- For higher FPS, you can make the FLIP_TEST false.
Please refer to HRNet's quick start
Testing on MPII dataset using model zoo's models(GoogleDrive)
python test.py \
--cfg experiments/mpii/lpn/lpn50_256x256_gd256x2_gc.yaml
Testing on COCO val2017 dataset using model zoo's models(GoogleDrive)
python test.py \
--cfg experiments/coco/lpn/lpn50_256x192_gd256x2_gc.yaml
[1] Simple Baselines for Human Pose Estimation and Tracking
[2] Deep High-Resolution Representation Learning for Human Pose Estimation