Table Of Contents
Table Of Contents

Pose Estimation

Visualization of Inference Throughputs vs. Validation AP of COCO pre-trained models is illustrated in the following graph. Throughputs are measured with single V100 GPU and batch size 64.

../_images/plot_help.png pose

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

Mobile Pose Models

By replacing the backbone network, and use pixel shuffle layer instead of deconvolution, we can have models that are very fast.

These models are suitable for edge device applications, tutorials on deployment will come soon.

Models are trained with input size 256x192, unless specified.

Model

OKS AP

OKS AP (with flip)

Hashtag

Training Command

Training log

mobile_pose_resnet18_v1b 1

66.2/89.2/74.3

67.9/90.3/75.7

dd6644eb

shell script

log

mobile_pose_resnet50_v1b 1

71.1/91.3/78.7

72.4/92.3/79.8

ec8809df

shell script

log

mobile_pose_mobilenet1.0 1

64.1/88.1/71.2

65.7/89.2/73.4

b399bac7

shell script

log

mobile_pose_mobilenetv2_1.0 1

63.7/88.1/71.0

65.0/89.2/72.3

4acdc130

shell script

log

mobile_pose_mobilenetv3_large 1

63.7/88.9/70.8

64.5/89.0/72.0

1ca004dc

shell script

log

mobile_pose_mobilenetv3_small 1

54.3/83.7/59.4

55.6/84.7/61.7

b1b148a9

shell script

log

AlphaPose

Checkout the demo tutorial here: 2. Predict with pre-trained AlphaPose Estimation models

Alpha Pose models are evaluated with input size (320*256), unless otherwise specified. Usage is similar to simple pose section.

Model

OKS AP

OKS AP (with flip)

Hashtag

Training Command

Training log

alpha_pose_resnet101_v1b_coco 2

74.2/91.6/80.7

76.7/92.6/82.9

de56b871

1(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)

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.

2

Fang, Hao-Shu, et al. “Rmpe: Regional multi-person pose estimation.” Proceedings of the IEEE International Conference on Computer Vision. 2017.