DECO

DECO: Query-Based End-to-End Object Detection with ConvNets

Xinghao Chen*, Siwei Li*, Yijing Yang, Yunhe Wang (*Equal Contribution)

arXiv 2023

[arXiv] [BibTeX]

Updates

• 2024/02/04: Pre-trained models and codes of DECO are released both in Pytorch and Mindspore.

Overview

Detection ConvNet (DECO) is a simple yet effective query-based end-to-end object detection framework, which is composed of a backbone and convolutional encoder-decoder architecture. Our DECO model enjoys the similar favorable attributes as DETR. We compare the proposed DECO against prior detectors on the challenging COCO benchmark. Despite its simplicity, our DECO achieves competitive performance in terms of detection accuracy and running speed. Specifically, with the ResNet-50 and ConvNeXt-Tiny backbone, DECO obtains 38.6% and 40.8% AP on COCO val set with 35 and 28 FPS respectively. We hope the proposed DECO brings another perspective for designing object detection framework.

Main Results

Here we provide the pretrained DECO weights.

Backbone	Pretrain	Epochs	Queries	AP (%)	Download
R-50	ImageNet-1k	150	100	38.8	deco_r50_150e

Installation

pip install torch==1.8.0 torchvision==0.9.0
pip install pycocotools
pip install timm

Training

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --backbone resnet50 --batch_size 2 --coco_path {your_path_to_coco} --output_dir {your_path_for_outputs} # 4 gpus example

By default, we use 4 GPUs with total batch size as 8 for training DECO with ResNet-50 backbone.

Evaluation

Model evaluation can be done as follows:

python eval.py --backbone resnet50 --batch_size 1 --coco_path {your_path_to_coco} --ckpt_path {your_path_to_pretrained_ckpt}

Results of DECO with ResNet-50 backbone:
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.588
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.411
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.199
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.431
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.555
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.522
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.556
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.798

Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

@misc{chen2023deco,
      title={DECO: Query-Based End-to-End Object Detection with ConvNets}, 
      author={Xinghao Chen and Siwei Li and Yijing Yang and Yunhe Wang},
      year={2023},
      eprint={2312.13735},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}