mitmul / Deeppose
Licence: gpl-2.0
DeepPose implementation in Chainer
Stars: ✭ 377
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python
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DeepPose
NOTE: This is not official implementation. Original paper is DeepPose: Human Pose Estimation via Deep Neural Networks.
Requirements
- Python 3.5.1+
- Chainer 1.13.0+
- numpy 1.9+
- scikit-image 0.11.3+
- OpenCV 3.1.0+
I strongly recommend to use Anaconda environment. This repo may be able to be used in Python 2.7 environment, but I haven't tested.
Installation of dependencies
pip install chainer
pip install numpy
pip install scikit-image
# for python3
conda install -c https://conda.binstar.org/menpo opencv3
# for python2
conda install opencv
Dataset preparation
bash datasets/download.sh
python datasets/flic_dataset.py
python datasets/lsp_dataset.py
python datasets/mpii_dataset.py
- FLIC-full dataset
- LSP Extended dataset
- MPII dataset
MPII Dataset
- MPII Human Pose Dataset
- training images: 18079, test images: 6908
- test images don't have any annotations
- so we split trining imges into training/test joint set
- each joint set has
- training joint set: 17928, test joint set: 1991
Start training
Starting with the prepared shells is the easiest way. If you want to run train.py
with your own settings, please check the options first by python scripts/train.py --help
and modify one of the following shells to customize training settings.
For FLIC Dataset
bash shells/train_flic.sh
For LSP Dataset
bash shells/train_lsp.sh
For MPII Dataset
bash shells/train_mpii.sh
GPU memory requirement
- AlexNet
- batchsize: 128 -> about 2870 MiB
- batchsize: 64 -> about 1890 MiB
- batchsize: 32 (default) -> 1374 MiB
- ResNet50
- batchsize: 32 -> 6877 MiB
Prediction
Will add some tools soon
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