Awesome
Mixture Density Object Detector: PyTorch Implementation
This repository is the PyTorch implementation of
"Density-based Object Detection: Learning Bounding Boxes without Ground Truth Assignment"
Environment
- python3
- pytorch1.7
Directory Structure
(root-directory)
├── README.md
├── run
├── src
│ └── (python-source-file.py)
├── result
│ └── (result-directory)
│ ├── ...
│ └── snapshot
│ └── (iteration)
│ ├── network.pth
│ └── optimizer.pth
└── data
└── coco-2017
├── annotations
└── images
You can download the voc and coco dataset in the follow links.
http://cocodataset.org/#download (coco-2017)
Usage
Training
# run/run_mdod_coco.sh
--training_args="{'max_iter': maximum number of iterations, ...}"
# command
.../(root-directory)$ bash run/run_mdod_coco.sh
Test
# run/run_mdod_coco.sh
--training_args="{'init_iter': 0, 'max_iter': 0, ...}",
--test_iters="[0]"
--load_dir="path of the snapshot directory that has a network.pth file"
# command
.../(root-directory)$ bash run/run_mdod_coco.sh
Citation
@article{yoo2019density,
title={Density-based Object Detection: Learning Bounding Boxes without Ground Truth Assignment},
author={Yoo, Jaeyoung and Lee, Hojun and Chung, Inseop and Seo, Geonseok and Kwak, Nojun},
journal={arXiv preprint arXiv:1911.12721},
year={2019}
}