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Trained Models & Performances

Models

I have tried multiple variations of models to find optmized network architecture. Some of them are below and checkpoint files are provided for research purpose.

  • cmu

    • the model based VGG pretrained network which described in the original paper.
    • I converted Weights in Caffe format to use in tensorflow.
    • pretrained weight download

  • dsconv

    • Same architecture as the cmu version except for the depthwise separable convolution of mobilenet.
    • I trained it using 'transfer learning', but it provides not-enough speed and accuracy.

  • mobilenet

    • Based on the mobilenet paper, 12 convolutional layers are used as feature-extraction layers.
    • To improve on small person, minor modification on the architecture have been made.
    • Three models were learned according to network size parameters.
    • I published models which is not the best ones, but you can test them before you trained a model from the scratch.

  • mobilenet v2

    • Similar to mobilenet, but using improved version of it.
Name Feature Layers Configuration
cmu VGG16 OpenPose
mobilenet_thin Mobilenet width=0.75 refine-width=0.75
mobilenet_v2_large Mobilenet v2 (582M) width=1.40 refine-width=1.00
mobilenet_v2_small Mobilenet v2 (97M) width=0.50 refine-width=0.50

Performance on COCO Datasets

Set Model Scale Resolution AP AP 50 AP 75 AP medium AP large AR AR 50 AR 75 AR medium AR large
2014 Val Original Paper 3 Image 0.584 0.815 0.626 0.544 0.651
2014 Val CMU(openpose) 1 Image 0.5067 0.7660 0.5377 0.4927 0.5309 0.5614 0.7900 0.5903 0.5089 0.6347
2014 Val VGG(openpose, our) 1 Image 0.5067 0.7660 0.5377 0.4927 0.5309 0.5614 0.7900 0.5903 0.5089 0.6347
2017 Val VGG(openpose, our) 1 Image 0.496 0.759 0.521 0.493 0.497 0.562 0.7830 0.590 0.506 0.644
2014 Val Mobilenet thin 1 Image 0.2806 0.5577 0.2474 0.2802 0.2843 0.3214 0.5840 0.2997 0.2946 0.3587
2014 Val Mobilenet-v2 Large 1 Image 0.3130 0.5846 0.2940 0.2622 0.3850 0.3680 0.6101 0.3637 0.2765 0.4912
2014 Val Mobilenet-v2 Small 1 Image 0.1730 0.4062 0.1240 0.1501 0.2105 0.2207 0.4505 0.1876 0.1601 0.3020
  • I also ran keras & caffe models to verify single-scale version's performance, they matched this result.

Computation Budget & Latency

Model mAP@COCO2014 GFLOPs Latency(432x368)
(Macbook 15' 2.9GHz i9, tf 1.12)		 Latency(432x368)
(V100 GPU)
CMU, VGG(OpenPose) 0.8589s 0.0570s
Mobilenet thin 0.2806 0.1701s 0.0217s
Mobilenet-v2 Large 0.3130 0.2066s 0.0214s
Mobilenet-v2 Small 0.1730 0.1290s 0.0210s

Optimized Tensorflow was built before run this experiment. This may varies between environments, images and other factors.