ldxray-mmdetection
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: rstao-bjtu
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 12.9 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Introduction
Our project extends the MMDetection framework to specialize in dual-view X-ray imaging. Unlike single-view datasets, dual-view imaging captures objects from both vertical and side perspectives, enhancing detection accuracy by reducing occlusions and providing more comprehensive views of the objects.
For more details, you can view our LDXray dataset introduction here.
How To Run
First, install our project following these steps:
bash
git clone https://github.com/SuZipei/LDXray-mmdetection.git
cd LDXray-mmdetection-main
pip install -v -e .
For more detailed installation instructions, please refer to the installation guide.
Second, download our LDXray dataset and place it in the LDXray/dual-view directory.
We provide a function Load2ImageFromFiles in mmdet/datasets/transforms/loading.py to load dual-view images from the LDXray dataset. Additionally, we have rewritten ImgDataPreprocessor in mmdet/models/data_preprocessors/data_preprocessor.py to normalize dual-view data.
Third, write a configuration file for your model. Examples of configuration files used in our research can be found in LDXray/config.
Finally, train or test your model. Please refer to the train and test guide for detailed instructions.
Citation
Please note that our dataset is built upon the MMDetection framework, please cite the MMDetection framework as follows:
plaintext
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal = {arXiv preprint arXiv:1906.07155},
year = {2019}
}
License
- For academic and non-commercial use only
- Apache License 2.0
Owner
- Name: rstao
- Login: rstao-bjtu
- Kind: user
- Repositories: 1
- Profile: https://github.com/rstao-bjtu
GitHub Events
Total
- Push event: 1
- Create event: 2
Last Year
- Push event: 1
- Create event: 2