Awesome
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
- This repository is an official implementation of the paper DQ-DETR: DETR with Dynamic Query for Tiny Object Detection.
- The original repository link was https://github.com/Katie0723/DQ-DETR. Here is the updated link.
News
[2024/12/06] We released the organized datasets AI-TOD-V1 and AI-TOD-V2.
[2024/7/1]: DQ-DETR has been accepted by ECCV 2024. π₯π₯π₯
[2024/5/3]: DNTR has been accepted by TGRS 2024. π₯π₯π₯
Installation -- Compiling CUDA operators
- The code are built upon the official DINO DETR repository.
conda create -n dqdetr python=3.9 --y
conda activate dqdetr
bash install.sh
<!-- # bash scripts/DQ_eval.sh /nfs/home/hoiliu/dqdetr/weights/dqdetr_best305.pth -->
Eval models
bash scripts/DQ_eval.sh /path/to/your/dataset /path/to/your/checkpoint
Trained Model
- Changed the pretrained model path in DQ.sh
CUDA_VISIBLE_DEVICES=5,6,7 bash scripts/DQ.sh /path/to/your/dataset
Our works on Tiny Object Detection
Title | Venue | Links |
---|---|---|
DNTR | TGRS 2024 | Paper | code |
DQ-DETR | ECCV 2024 | Paper | code |
Performance
Table 1. Training Set: AI-TOD-V2 trainval set, Testing Set: AI-TOD-V2 test set, 36 epochs, where FRCN, DR denotes Faster R-CNN and DetectoRS, respectively.
Method | Backbone | mAP | AP<sub>50</sub> | AP<sub>75</sub> | AP<sub>vt</sub> | AP<sub>t</sub> | AP<sub>s</sub> | AP<sub>m</sub> |
---|---|---|---|---|---|---|---|---|
Faster R-CNN | R-50 | 11.1 | 26.3 | 7.6 | 0.0 | 7.2 | 23.3 | 33.6 |
NWD-RKA | R-50 | 23.4 | 53.5 | 16.8 | 8.7 | 23.8 | 28.5 | 36.0 |
DAB-DETR | R-50 | 22.4 | 55.6 | 14.3 | 9.0 | 21.7 | 28.3 | 38.7 |
DINO-DETR | R-50 | 25.9 | 61.3 | 17.5 | 12.7 | 25.3 | 32.0 | 39.7 |
DQ-DETR | R-50 | 30.5 | 69.2 | 22.7 | 15.2 | 30.9 | 36.8 | 45.5 |
AI-TOD-v1 and AI-TOD-v2 Datasets
- Step 1: Download the datasets from the below link.
https://drive.google.com/drive/folders/1hkbcZ3TPABx3QxoCufE1KAPu55Ibw-8d?usp=sharing
- Step 2: Organize the downloaded files in the following way.
ββ Dataset
β ββ aitod
β ββ annotations
β ββ images
β ββ test
β ββ train
β ββ trainval
β ββ val
ββ DQ-DETR
Pretrained Weights
-
Referred to checkpoint.txt for more details.
Citation
@InProceedings{huang2024dq,
author={Huang, Yi-Xin and Liu, Hou-I and Shuai, Hong-Han and Cheng, Wen-Huang},
title={DQ-DETR: DETR withΒ Dynamic Query forΒ Tiny Object Detection},
booktitle={European Conference on Computer Vision},
pages={290--305},
year={2025},
organization={Springer}
}
@ARTICLE{10518058,
author={Liu, Hou-I and Tseng, Yu-Wen and Chang, Kai-Cheng and Wang, Pin-Jyun and Shuai, Hong-Han and Cheng, Wen-Huang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={A DeNoising FPN With Transformer R-CNN for Tiny Object Detection},
year={2024},
volume={62},
number={},
pages={1-15},
}