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https://github.com/xinghaochen/deco
Official PyTorch implementation of "DECO: Query-Based End-to-End Object Detection with ConvNets"
https://github.com/xinghaochen/deco
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Official PyTorch implementation of "DECO: Query-Based End-to-End Object Detection with ConvNets"
- Host: GitHub
- URL: https://github.com/xinghaochen/deco
- Owner: xinghaochen
- Created: 2024-02-04T14:09:55.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-02-08T09:51:00.000Z (11 months ago)
- Last Synced: 2024-02-08T10:41:59.528Z (11 months ago)
- Language: Python
- Homepage:
- Size: 547 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DECO
### **DECO: Query-Based End-to-End Object Detection with ConvNets**
*Xinghao Chen\*, Siwei Li\*, Yijing Yang, Yunhe Wang (\*Equal Contribution)*
*arXiv 2023*
[[`arXiv`](https://arxiv.org/abs/2312.13735)] [[`BibTeX`](#citation)]
## 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](https://github.com/xinghaochen/DECO) and [Mindspore](https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/DECO).
## 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}
}