Xinghao Chen*, Siwei Li*, Yijing Yang, Yunhe Wang (*Equal Contribution)
arXiv 2023
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.
Here we provide the pretrained DECO weights.
| Backbone | Pretrain | Epochs | Queries | AP (%) | Download |
|---|---|---|---|---|---|
| R-50 | ImageNet-1k | 150 | 100 | 38.8 | deco_r50_150e |
pip install torch==1.8.0 torchvision==0.9.0
pip install pycocotools
pip install timmpython -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 exampleBy default, we use 4 GPUs with total batch size as 8 for training DECO with ResNet-50 backbone.
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 ] = 0.798If 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}
}
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