This repository includes the official implementation of the paper:
Dynamic Color Transform Networks for Wheat Head Detection
Plant Phenomics, 2022
& Dynamic Color Transform for Wheat Head Detection
International Conference on Computer Vision Workshops (ICCVW), 2021
Chengxin Liu, Kewei Wang, Hao Lu, Zhiguo Cao
Huazhong University of Science and Technology, China
- DCT is intuitive and steerable
- We won 2nd Place in Global Wheat Challenge 2021 based on DCT
- DCT is also applicable to other vision tasks, e.g., crowd counting
- Set up environment
# env
conda create -n dct python=3.7
conda activate dct
# install pytorch
conda install pytorch==1.10.0 torchvision==0.11.0 -c pytorch -c conda-forge
- Install
# clone
git clone https://github.com/cxliu0/DCT.git
cd DCT
# install dependecies
pip install -r requirements/build.txt
- Download Global Wheat Head Detection Dataset 2021 (GWHD 2021). We expect the direcory structure to be as follow:
DCT
├── data
│ ├── gwhd_2021
│ ├── images
│ ├── competition_test.csv
│ ├── competition_train.csv
│ ├── competition_val.csv
├── configs
├── models
├── ...
- Preprocess data
python preprocess_data.py
- Download pretrained yolov4-p7 model and put it into
./pretrained/
We follow a two-step training stretegy.
- Step 1: train a baseline Scaled-YOLOv4 model
sh train_baseline.sh
- Step 2: fix the weights of the baseline model, and train DCT:
sh train_dct.sh
We implement the evaluation of ADA following Global Wheat Challenge 2021 (GWC 2021). Note that the tested ADA of this repository seems slightly lower than the official one.
- Modify test_ADA.sh
weights
: modify it to the path of your locally trained model
Alternatively, pretrained DCT model is available at Google Drive.
- Run
sh test_ADA.sh
If you find this work or code useful for your research, please consider citing:
@article{liu2022dct,
title = {Dynamic Color Transform Networks for Wheat Head Detection},
author = {Chengxin Liu and Kewei Wang and Hao Lu and Zhiguo Cao },
journal = {Plant Phenomics},
year={2022}
}
@INPROCEEDINGS{liu2021dct,
author={Liu, Chengxin and Wang, Kewei and Lu, Hao and Cao, Zhiguo},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
title={Dynamic Color Transform for Wheat Head Detection},
year={2021},
pages={1278-1283},
}
This repository is based on Scaled-YOLOv4.