DynamicDet [arXiv]
This repo contains the official implementation of "DynamicDet: A Unified Dynamic Architecture for Object Detection".
MS COCO
| Model | Easy / Hard | Size | FLOPs | FPS | APval | APtest |
|---|---|---|---|---|---|---|
| Dy-YOLOv7 | 90% / 10% | 640 | 112.4G | 110 | 51.4% | 52.1% |
| 50% / 50% | 640 | 143.2G | 96 | 52.7% | 53.3% | |
| 10% / 90% | 640 | 174.0G | 85 | 53.3% | 53.8% | |
| 0% / 100% | 640 | 181.7G | 83 | 53.5% | 53.9% | |
| Dy-YOLOv7-X | 90% / 10% | 640 | 201.7G | 98 | 53.0% | 53.3% |
| 50% / 50% | 640 | 248.9G | 78 | 54.2% | 54.4% | |
| 10% / 90% | 640 | 296.1G | 65 | 54.7% | 55.0% | |
| 0% / 100% | 640 | 307.9G | 64 | 54.8% | 55.0% | |
| Dy-YOLOv7-W6 | 90% / 10% | 1280 | 384.2G | 74 | 54.9% | 55.2% |
| 50% / 50% | 1280 | 480.8G | 58 | 55.9% | 56.1% | |
| 10% / 90% | 1280 | 577.4G | 48 | 56.4% | 56.7% | |
| 0% / 100% | 1280 | 601.6G | 46 | 56.5% | 56.8% |
Table Notes
-
FPS is measured on the same machine with 1 NVIDIA V100 GPU, with
batch_size = 1,no_traceandfp16. -
More results can be found on the paper.
cd DynamicDet
conda install pytorch=1.11 cudatoolkit=11.3 torchvision -c pytorch
pip install -r requirements.txtDownload MS COCO dataset images (train, val, test) and labels.
├── coco
│ ├── train2017.txt
│ ├── val2017.txt
│ ├── test-dev2017.txt
│ ├── images
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── labels
│ │ ├── train2017
│ │ ├── val2017
│ ├── annotations
│ │ ├── instances_val2017.json
Step1: Training cascaded detector
-
Single GPU training
python train_step1.py --workers 8 --device 0 --batch-size 16 --epochs 300 --img 640 --cfg cfg/dy-yolov7-step1.yaml --weight '' --data data/coco.yaml --hyp hyp/hyp.scratch.p5.yaml --name dy-yolov7-step1 -
Multiple GPU training (OURS, RECOMMENDED 🚀)
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_step1.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --epochs 300 --img 640 --cfg cfg/dy-yolov7-step1.yaml --weight '' --data data/coco.yaml --hyp hyp/hyp.scratch.p5.yaml --name dy-yolov7-step1
Step2: Training adaptive router
python train_step2.py --workers 4 --device 0 --batch-size 1 --epochs 2 --img 640 --adam --cfg cfg/dy-yolov7-step2.yaml --weight runs/train/dy-yolov7-step1/weights/last.pt --data data/coco.yaml --hyp hyp/hyp.finetune.dynamic.adam.yaml --name dy-yolov7-step2python get_dynamic_thres.py --device 0 --batch-size 1 --img-size 640 --cfg cfg/dy-yolov7-step2.yaml --weight weights/dy-yolov7.pt --data data/coco.yaml --task valpython test.py --img-size 640 --batch-size 1 --conf 0.001 --iou 0.65 --device 0 --cfg cfg/dy-yolov7-step2.yaml --weight weights/dy-yolov7.pt --data data/coco.yaml --dy-thres <DY_THRESHOLD>python detect.py --cfg cfg/dy-yolov7-step2.yaml --weight weights/dy-yolov7.pt --num-classes 80 --source <IMAGE/VIDEO> --device 0 --dy-thres <DY_THRESHOLD>If you find this repo useful in your research, please consider citing the following paper:
@inproceedings{lin2023dynamicdet,
title={DynamicDet: A Unified Dynamic Architecture for Object Detection},
author={Lin, Zhihao and Wang, Yongtao and Zhang, Jinhe and Chu, Xiaojie},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact [email protected].