Table Of Contents
Table Of Contents

Pose Estimation

Note

Pose Estimation is released in GluonCV 0.4. Please be sure to update your installation by pip install gluoncv --upgrade to try it out.

MS COCO Keypoints

Hint

The training commands work with the following scripts:

Hint

For COCO dataset, training imageset is train2017 and validation imageset is val2017.

The COCO metric, Average Precision (AP) with IoU threshold 0.5:0.95 (averaged 10 values, AP 0.5:0.95), 0.5 (AP 0.5) and 0.75 (AP 0.75) are reported together in the format (AP 0.5:0.95)/(AP 0.5)/(AP 0.75).

COCO keypoints metrics evaluate Object Keypoint Similarity AP. Please read the official doc for detailed introduction.

By averaging the prediction from the original input and the flipped one, we can get higher performance. Here we report the performance for predictions with and without the flip ensemble.

Simple Pose with ResNet

Checkout the demo tutorial here: 1. Predict with pre-trained Simple Pose Estimation models

Most models are trained with input size 256x192, unless specified. Parameters with a grey name can be downloaded by passing the corresponding hashtag.

  • Download default pretrained weights: net = get_model('simple_pose_resnet152_v1d', pretrained=True)
  • Download weights given a hashtag: net = get_model('simple_pose_resnet152_v1d', pretrained='2f544338')
Model OKS AP OKS AP (with flip) Hashtag Training Command Training log
simple_pose_resnet18_v1b [1] 66.3/89.2/73.4 68.4/90.3/75.7 f63d42ac shell script log
simple_pose_resnet18_v1b [1] (128x96) 52.8/83.6/57.9 54.5/84.8/60.3 ccd24037 shell script log
simple_pose_resnet50_v1b [1] 71.0/91.2/78.6 72.2/92.2/79.9 e2c7b1ad shell script log
simple_pose_resnet50_v1d [1] 71.6/91.3/78.7 73.3/92.4/80.8 ba2675b6 shell script log
simple_pose_resnet101_v1b [1] 72.4/92.2/79.8 73.7/92.3/81.1 b7ec0de1 shell script log
simple_pose_resnet101_v1d [1] 73.0/92.2/80.8 74.2/92.4/82.0 1f8f48fd shell script log
simple_pose_resnet152_v1b [1] 72.4/92.1/79.6 74.2/92.3/82.1 ef4e0336 shell script log
simple_pose_resnet152_v1d [1] 73.4/92.3/80.7 74.6/93.4/82.1 3ca502ea shell script log
simple_pose_resnet152_v1d [1] (384x288) 74.8/92.3/82.0 76.1/92.4/83.2 2f544338 shell script log
[1](1, 2, 3, 4, 5, 6, 7, 8, 9, 10) Xiao, Bin, Haiping Wu, and Yichen Wei. “Simple baselines for human pose estimation and tracking.” Proceedings of the European Conference on Computer Vision (ECCV). 2018.