Awesome

update 2019-08-14

Add tensorflow 2.0 version, link here. Repo still need to update. Welcome to raise questions.

update 2019-04-01

• Add person body mask information. No loss will be added where there is no person annotations in image.
• Increase paf loss weight in total loss. Previous cpm_loss : paf_loss == 1:1 and now is '1:2'

update 2019-03-20

Add evaluation part. The evaluation based on ai-challenger evaluation solution. More information please refer to this link.

Easy way for how to use it:

• First, you need run test&model/model_json.py to generate a json file. The param in this file contains everything you need. Remember that param['img_path] && param['jason_file'] parameters is the groundtruth test files that you need to test.

• Then, run test&model/model_eval.py, the command line would be like this:

    python model_eval.py --submit predictions.json --ref groundtruth.json
  

  this will give you a score that about your model performance, between 0~1, 0 is worst and 1 is best. Make sure that you need run python2 instead of python3 because some errors will occur in eval.py file.

update 2019-03-18

Based on official pytorch implementation, re-write tf-model, see lightweight_openpose.py for detailed information, corresponding net structure picture is named lightweight.png. New pre_trained model will be upload soon.

A tensorflow implementation about Arxiv Paper "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose"

Thanks for the author provide PyTorch version

(This repo is more useful than mine i think, enjoy it!) Pytorch implementation.

trained model

• upload 2019-02-13, in model folder, model.ckpt-1008540.*.
• upload 2019-03-18, in modelfolder, named model.ckpt-61236, on ai_test_A dataset, get score 0.0377, so bad.
• someone who wants to use this lightweight_openpose model on your own dataset, please train it by yourself. The trained model upload only use to test but not good enough to use in practice. I did not trained it good enough. Pleas make sure that.

update

The original caffe prototxt(provided by paper author) has been upload, you can found in repo file named "lightweight_openpose.prototxt"

Requirement

• tensorflow >= 1.11.0
• python 3.6+
• cuda && cudnn
• imgaug

Train

python3 train.py

all parameters has been set in src/train_config.py.

Train Dataset

we use ai-challenger format dataset, which can found in this website.

Note

• after training one epoch done, one validation epoch will be executed. So we set the dataset.repeat(1). And use make_initializable_iterator() instead of `make_one_shot_iterator().
• we modified some implementation about this model which inspired by this article. we not only send previous stage output information to next stage as its input, but also used this previous output information to add next stage output featuremap in order to get next final output, which can be found in article.