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DeepFlux for Skeletons in the Wild, CVPR 2019 [Paper]
Citation
Please cite the related works in your publications if it helps your research:
@inproceedings{wang2019deepflux,
title={DeepFlux for Skeletons in the Wild},
author={Wang, Yukang and Xu, Yongchao and Tsogkas, Stavros and Bai, Xiang and Dickinson, Sven and Siddiqi, Kaleem},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5287--5296},
year={2019}
}
cp Makefile.config.example Makefile.config
# adjust Makefile.config (for example, enable python layer)
make all -j16
# make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make pycaffe
# download datasets and pretrained model then
mkdir data && mv [your_dataset_folder] data/
mkdir models && mv [your_pretrained_model] models/
# data augmentationcd data/[your_dataset_folder]
matlab -nodisplay -r "run augmentation.m; exit"
3. Training scripts
# an example on SK-LARGE datasetcd examples/DeepFlux/
python train.py --gpu [your_gpu_id] --dataset sklarge --initmodel ../../models/VGG_ILSVRC_16_layers.caffemodel
For training an end-to-end version of DeepFlux, adding the --e2e option.
4. Evaluation scripts
# an example on SK-LARGE datasetcd evaluation/
# inference with C++
./eval_cpp.sh ../../data/SK-LARGE/images/test ../../data/SK-LARGE/groundTruth/test ../../models/sklarge_iter_40000.caffemodel
# inference with Python
./eval_py.sh ../../data/SK-LARGE/images/test ../../data/SK-LARGE/groundTruth/test ../../models/sklarge_iter_40000.caffemodel
# inference with Python (end-to-end version)
./eval_py_e2e.sh ../../data/SK-LARGE/images/test ../../data/SK-LARGE/groundTruth/test ../../models/sklarge_iter_40000.caffemodel
