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All Projects → aditya-vora → Fchd Fully Convolutional Head Detector

aditya-vora / Fchd Fully Convolutional Head Detector

Licence: other
Code for FCHD - A fast and accurate head detector

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningconvolutional-neural-networksfaster-rcnn

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FCHD-Fully-Convolutional-Head-Detector

Code for FCHD - A fast and accurate head detector

This is the code for FCHD - A Fast and accurate head detector. See the paper for details and video for demo.

Dependencies

  • 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-cuda80 or(cupy-cuda90,cupy-cuda91, etc).

  • install visdom for visualization, refer to their github page

Installation

  1. Install Pytorch

  2. Clone this repository

git clone https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
  1. Build cython code for speed:
cd src/nms/
python build.py build_ext --inplace

Training

  1. Download the caffe pre-trained VGG16 from the following link. Store this pre-trained model in data/pretrained_model folder.

  2. Download the BRAINWASH dataset from the official website. Unzip it and store the dataset in the data/ folder.

  3. Make appropriate settings in src/config.py file regarding the updated paths.

  4. Start visdom server for visualization:

python -m visdom.server
  1. Run the following command to train the model: python train.py.

Demo

  1. Download the best performing model from the following link.

  2. Store the head detection model in checkpoints/ folder.

  3. Run the following python command from the root folder.

python head_detection_demo.py --img_path <test_image_name> --model_path <model_path>

Results

Method AP
Overfeat - AlexNet [1] 0.62
ReInspect, Lfix [1] 0.60
ReInspect, Lfirstk [1] 0.63
ReInspect, Lhungarian [1] 0.78
Ours 0.70

Runtime

  • Runs at 5fps on NVidia Quadro M1000M GPU with 512 CUDA cores.

Acknowledgement

This work builds on many of the excellent works:

Reference

[1] 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.

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