DECO
DECO: Query-Based End-to-End Object Detection with ConvNets
Xinghao Chen*, Siwei Li*, Yijing Yang, Yunhe Wang (*Equal Contribution)
arXiv 2023
Updates
- 2024/10/03: Update pre-trained models and codes for DECO+.
- 2024/10/02: Update pre-trained models and codes for ConvNeXt backbone.
- 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. | Detector | Backbone | Epochs | Queries| AP (%) | Download | | ------------------- | -------- | ------- |------- |------- |------- | | DECO | R-50 | 150 | 100 | 38.8 | [deco_r50_150e.pth](https://github.com/xinghaochen/DECO/releases/download/1.0/deco_r50_150e.pth) | DECO | ConvNeXt-Tiny | 150 | 100 | 40.8 | [deco_convnextTiny1K_150.pth](https://github.com/xinghaochen/DECO/releases/download/1.0/deco_convnextTiny1K_150.pth) | DECO+ | R-18 | 150 | - | 40.7 | [decoplus_r18_150e.pth](https://github.com/xinghaochen/DECO/releases/download/1.0/decoplus_r18_150e.pth) | DECO+ | R-50 | 150 | - | 47.9 | [decoplus_r50_150e.pth](https://github.com/xinghaochen/DECO/releases/download/1.0/decoplus_r50_150e.pth) ## DECO ### Installation ```bash pip install torch==1.8.0 torchvision==0.9.0 pip install pycocotools pip install timm ``` ### Training ```bash cd deco 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: ```bash python eval.py --backbone resnet50 --batch_size 1 --coco_path {your_path_to_coco} --ckpt_path {your_path_to_pretrained_ckpt} ``` ```bash 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 ``` ```bash Results of DECO with ConvNeXt-Tiny backbone: IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.436 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.805 ``` ## DECO+ ### Installation ```bash conda create --name decoplus --python=3.8 conda activate decoplus pip install -r requirements.txt ``` ### Training To train a DECO+ model on a single node with 8 gpus: ```bash cd deco_plus python -m torch.distributed.launch --nproc_per_node=8 --use_env ./tools/train.py -c configs/decoplus/decoplus_r18.yml ``` ### Evaluation Model evaluation can be done as follows: ```bash python eval.py --config configs/decoplus/decoplus_r18.yml --coco_path {your_path_to_pretrained_ckpt}/decoplus_r18_150e.pth ``` Results of DECO+ with ResNet-18d backbone: ```bash IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.407 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.589 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.443 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.562 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.636 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.811 ``` Results of DECO+ with ResNet-50d backbone: ```bash IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.672 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.524 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.313 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.520 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.511 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.728 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.845 ``` ## Citing DECO If you find our work helpful for your research, please consider citing the following BibTeX entry. ```BibTex @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} }