Code for FCHD - A fast and accurate head detector
The code is tested on Ubuntu 16.04.
install PyTorch >=0.4 with GPU (code are GPU-only), refer to official website
install cupy, you can install via
pip install cupy-cuda80or(cupy-cuda90,cupy-cuda91, etc).
install visdom for visualization, refer to their github page
Clone this repository
git clone https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
- Build cython code for speed:
cd src/nms/ python build.py build_ext --inplace
Download the caffe pre-trained VGG16 from the following link. Store this pre-trained model in
Download the BRAINWASH dataset from the official website. Unzip it and store the dataset in the
Make appropriate settings in
src/config.pyfile regarding the updated paths.
Start visdom server for visualization:
python -m visdom.server
- Run the following command to train the model:
Download the best performing model from the following link.
Store the head detection model in
Run the following python command from the root folder.
python head_detection_demo.py --img_path <test_image_name> --model_path <model_path>
|Overfeat - AlexNet ||0.62|
|ReInspect, Lfix ||0.60|
|ReInspect, Lfirstk ||0.63|
|ReInspect, Lhungarian ||0.78|
- Runs at 5fps on NVidia Quadro M1000M GPU with 512 CUDA cores.
This work builds on many of the excellent works:
 Stewart, Russell, Mykhaylo Andriluka, and Andrew Y. Ng. "End-to-end people detection in crowded scenes." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.