Human pose estimation via Convolutional Part Heatmap Regression
Note: New code capable of running on devices with limited resources, for Human Pose Estimation and Face Alignment was released. For a demo please check: https://github.com/1adrianb/binary-human-pose-estimation
- Install the latest Torch version
- Install python 2.7 using the package manager
Most of the listed package can be installed by simple running
luarocks install [packagename]
For sh and fb.python packages please visit their github repositories and carrefully follow the instruction provided by their authors.
By default, on the first run the scripts will attempt to automatically download the models, however for your convinience they are provided also for separate usage.
|Dataset used||LSP error||MPII error|
|MPII + LSP||90.7||-|
The provided code comes along with a series of options. In order to list them please run:
th main.lua --help
To run a demo on 10 random images:
th main.lua -dataset lsp
To evaluate the model on the validation set for LSP/MPII:
th main.lua -dataset lsp -eval
If you have installed cudnn4 or cudnn5 you can run the demo faster:
th main.lua -dataset lsp -eval -usecudnn
The demo doesn't require a GPU, however having one will speed up the process.
Warning: The script will download by default both the models and the dataset(~15Gb), if you wan't to avoid this or you already have them downloaded please move them in the corresponding folders in datasets/[datasetname]_dataset/. Running the demo for lsp dataset will require only ~700Mb of space.