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Shih-En Wei 39698a882d | 7 years ago | |
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dataset/MPI/Tompson_valid | 8 years ago | |
model | 8 years ago | |
testing | 8 years ago | |
training | 8 years ago | |
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caffePath.cfg.example | 8 years ago | |
get_data.sh | 7 years ago |
Shih-En Wei, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh, "Convolutional Pose Machines", CVPR 2016.
This project is licensed under the terms of the GPL v2 license. By using the software, you are agreeing to the terms of the license agreement.
Contact: shihenw@cmu.edu.
make matcaffe
and make pycaffe
.caffePath.cfg.example
to caffePath.cfg
and set your own path in it.testing/get_model.sh
to retreive trained models from our web server.CPM_demo.m
: Put the testing image into sample_image
then run it! You can select models (we provided 4) or other parameters in config.m
. If you just want to try our best-scoring model, leave them default.CPM_benchmark.m
: Run the model on test benchmark and see the scores. Prediction files will be saved in testing/predicts
.get_data.sh
to get datasets including FLIC Dataset, LEEDS Sport Dataset and its extended training set, and MPII Dataset.genJSON(<dataset_name>)
to generate a json file in training/json/
folder. Dataset name can be MPI
, LEEDS
, or FLIC
. The json files contain raw informations needed for training from each individual dataset.python genLMDB.py
to generate LMDBs for CPM data layer in our caffe. Change the main function to select dataset, and note that you can generate a LMDB with multiple datasets.python genProto.py
to get prototxt for caffe. Read further explanation for layer parameters.Please cite CPM in your publications if it helps your research:
@inproceedings{wei2016cpm,
author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
booktitle = {CVPR},
title = {Convolutional pose machines},
year = {2016}
}
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https://readpaper.com/paper/2964304707
Unity3D Asset MATLAB Text Python other
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