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. 🔥🔥🔥

[2024/5/3]: DNTR has been accepted by TGRS 2024. 🔥🔥🔥

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

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	AP50	AP75	APvt	APt	APs	APm
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/1CowS5BrujefWQxxlmOFfUuLOfUUm8w6U?usp=sharing

• Step 2: Organize the downloaded files in the following way.

├─ Dataset
│   └─ aitod
│       ├─ annotations
│       ├─ images
│       ├─ test
│       ├─ train
│       ├─ trainval
│       └─ val
├─ DQ-DETR

Pretrained Weights

• Referred to checkpoint.txt for more details.

• https://reurl.cc/NlvV2Q

Citation

@InProceedings{huang2024dq,
author={Huang, Yi-Xin and Liu, Hou-I and Shuai, Hong-Han and Cheng, Wen-Huang},
title={DQ-DETR: DETR with Dynamic Query for 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},
}