# FCHD-Fully-Convolutional-Head-Detector

**Repository Path**: stevent095/FCHD-Fully-Convolutional-Head-Detector

## Basic Information

- **Project Name**: FCHD-Fully-Convolutional-Head-Detector
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-11-17
- **Last Updated**: 2024-06-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://arxiv.org/abs/1809.08766) for details and [video](https://youtu.be/gRPA7Hqk3VQ) 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](http://pytorch.org)

- 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](https://github.com/facebookresearch/visdom)

## Installation
1) Install Pytorch

2) Clone this repository
  ```Shell
  git clone https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
  ```
3) Build cython code for speed:
  ```Bash
  cd src/nms/
  python build.py build_ext --inplace
  ```

## Training
1) Download the caffe pre-trained VGG16 from the following [link](https://drive.google.com/open?id=10AwNitG-5gq-YEJcG9iihosiOu7vAnfO). Store this pre-trained model in `data/pretrained_model ` folder.
 
2) Download the BRAINWASH dataset from the [official website](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/software-and-datasets/). 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:
```Bash
python -m visdom.server
```
5) Run the following command to train the model: `python train.py `.

## Demo
1) Download the best performing model from the following [link](https://drive.google.com/open?id=1DbE4tAkaFYOEItwuIQhlbZypuIPDrArM). 

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

3) Run the following python command from the root folder. 
```Shell
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:
- [Simple faster rcnn pytorch implementation](https://github.com/chenyuntc/simple-faster-rcnn-pytorch) by [Yun Chen](https://github.com/chenyuntc).
- [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) by [Ross Girshick](https://github.com/rbgirshick).


## 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.