Deep Learning for Human Part Discovery in Images - Chainer implementation

NOTE: This is not official implementation. Original paper is Deep Learning for Human Part Discovery in Images.

We are now reproducing the experiments in the original paper. Any contribution will be welcomed!

Requirements

Python 2.7.11+
- Chainer 1.10+
- numpy 1.9+
- scipy 0.16+
- six
- matplotlib
- tqdm
- cv2 (opencv)

Preparation

Data

bash prepare.sh

This script downloads VOC 2010 dataset (http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar) and the authors' original dataset (http://www2.informatik.uni-freiburg.de/~oliveira/datasets/Sitting.tar.gz).

Model

You can download pre-trained FCN model from here.

We will use weights of this model and train new model on VOC dataset.

Start training

python train.py -g 0 -b 3 -e 3000 -l on -s on

Possible options

python train.py --help

GPU memory requirement

Citation from the original paper:

Each minibatch consists of just one image. The learning rate and momentum are fixed to 1e 10 and 0.99, respectively. We train the refinement layer by layer, which takes two days per refinement layer. Thus, the overall training starting from the pre-trained VGG network took 10 days on a single GPU.

Current maximum batchsize is 3 for 12 GB memory GPU.

Also it was confirmed that MBP (Late 2016, memory 16 GiB) can run with batchsize 1.

Result

Now in prep.

Visualize Prediction

python visualize.py -f PATH_TO_IMAGE_FILE

LICENSE

MIT LICENSE.

Author

shiba24, August 2016.

Contributors

bobye

CompN3rd/deep-learning-for-human-part-discovery-in-images