GitHub - aditya-vora/FCHD-Fully-Convolutional-Head-Detector: Code for FCHD - A f...
source link: https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
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. The manuscript is under review in a journal. The full code is implemented in Python with PyTorch framework. See the video on this link for demo.
Dependencies
-
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
-
Install Pytorch
-
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
Training
-
Download the caffe pre-trained VGG16 from the following link. Store this pre-trained model in
data/pretrained_model
folder. -
Download the BRAINWASH dataset from the official website. Unzip it and store the dataset in the
data/
folder. -
Make appropriate settings in
src/config.py
file regarding the updated paths. -
Start visdom server for visualization:
python -m visdom.server
- Run the following command to train the model:
python train.py
.
Demo
-
Download the best performing model from the following link.
-
Store the head detection model in
checkpoints/
folder. -
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 0.70Runtime
- 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.
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK