https://github.com/chasel-tsui/mmdet-aitod
Official implementation of AI-TOD-v2 and NWD-RKA
Science Score: 10.0%
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Repository
Official implementation of AI-TOD-v2 and NWD-RKA
Basic Info
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- Forks: 8
- Open Issues: 5
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Metadata Files
README.md
TODbox (Tiny Object Detection Box)
We have now released the full sets (trainval, test) of AI-TOD-v2! [Download]
This is a repository of the official implementation of the following paper: * [Paper][Code] Detecting tiny Objects in aerial images: A normalized Wasserstein distance and A new benchmark (ISPRS J P & RS, 2022) * [Paper][Code] Dot distance for tiny object detection in aerial images (CVPRW, 2021)
Introduction
The Normalized Wasserstein Distance and the RanKing-based Assigning strategy (NWD-RKA) for tiny object detection.
A comparison between AI-TOD and AI-TOD-v2.
Supported Data
Notes: The images of the AI-TOD-v2 are the same of the AI-TOD. In this stage, we only release the train, val annotations of the AI-TOD-v2, the test annotations will be used to hold further competitions.
Supported Methods
Supported baselines for tiny object detection: - [x] Baselines
Supported horizontal tiny object detection methods: - [x] DotD - [x] NWD-RKA - [ ] RFLA
Supported rotated tiny object detection methods: - [ ] DCFL
Installation and Get Started
Required environments: * Linux * Python 3.6+ * PyTorch 1.3+ * CUDA 9.2+ * GCC 5+ * MMCV * cocoapi-aitod
Install TODbox:
Note that our TODbox is based on the MMDetection 2.24.1. Assume that your environment has satisfied the above requirements, please follow the following steps for installation.
shell script
git clone https://github.com/Chasel-Tsui/mmdet-aitod.git
cd mmdet-nwdrka
pip install -r requirements/build.txt
python setup.py develop
Citation
If you use this repo in your research, please consider citing these papers.
``` @inproceedings{xu2021dot, title={Dot Distance for Tiny Object Detection in Aerial Images}, author={Xu, Chang and Wang, Jinwang and Yang, Wen and Yu, Lei}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, pages={1192--1201}, year={2021} }
@inproceedings{NWDRKA2022ISPRS, title={Detecting Tiny Objects in Aerial Images: A Normalized Wasserstein Distance and A New Benchmark}, author={Xu, Chang and Wang, Jinwang and Yang, Wen and Yu, Huai and Yu, Lei and Xia, Gui-Song}, booktitle={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={190}, pages={79--93}, year={2022}, } ```
References
Owner
- Name: Chang Xu
- Login: Chasel-Tsui
- Kind: user
- Location: Wuhan
- Website: https://chasel-tsui.github.io/Homepage/
- Repositories: 3
- Profile: https://github.com/Chasel-Tsui
Wuhan University
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