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https://github.com/mitmul/deeppose
DeepPose implementation in Chainer
https://github.com/mitmul/deeppose
chainer
Last synced: 8 days ago
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DeepPose implementation in Chainer
- Host: GitHub
- URL: https://github.com/mitmul/deeppose
- Owner: mitmul
- License: gpl-2.0
- Created: 2014-11-11T04:35:32.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2019-11-05T13:14:06.000Z (almost 5 years ago)
- Last Synced: 2024-10-14T12:37:02.153Z (20 days ago)
- Topics: chainer
- Language: Python
- Homepage: http://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/42237.pdf
- Size: 137 KB
- Stars: 408
- Watchers: 32
- Forks: 129
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepPose
NOTE: This is not official implementation. Original paper is [DeepPose: Human Pose Estimation via Deep Neural Networks](http://arxiv.org/abs/1312.4659).
# Requirements
- Python 3.5.1+
- [Chainer 1.13.0+](https://github.com/pfnet/chainer)
- 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](http://vision.grasp.upenn.edu/cgi-bin/index.php?n=VideoLearning.FLIC)
- [LSP Extended dataset](http://www.comp.leeds.ac.uk/mat4saj/lspet_dataset.zip)
- **MPII dataset**
- [Annotation](http://datasets.d2.mpi-inf.mpg.de/leonid14cvpr/mpii_human_pose_v1_u12_1.tar.gz)
- [Images](http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz)
## MPII Dataset
- [MPII Human Pose Dataset](http://human-pose.mpi-inf.mpg.de/#download)
- 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