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https://github.com/VDIGPKU/DynamicDet
[CVPR 2023] DynamicDet: A Unified Dynamic Architecture for Object Detection
https://github.com/VDIGPKU/DynamicDet
coco dynamic-neural-network object-detection yolo
Last synced: 3 months ago
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[CVPR 2023] DynamicDet: A Unified Dynamic Architecture for Object Detection
- Host: GitHub
- URL: https://github.com/VDIGPKU/DynamicDet
- Owner: VDIGPKU
- Created: 2023-04-11T06:08:46.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-01T04:03:33.000Z (11 months ago)
- Last Synced: 2024-08-03T01:17:52.594Z (7 months ago)
- Topics: coco, dynamic-neural-network, object-detection, yolo
- Language: Python
- Homepage:
- Size: 669 KB
- Stars: 109
- Watchers: 5
- Forks: 14
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - DynamicDet
- awesome-yolo-object-detection - DynamicDet
README
# DynamicDet [[arXiv]](https://arxiv.org/abs/2304.05552)
This repo contains the official implementation of [**"DynamicDet: A Unified Dynamic Architecture for Object Detection"**](https://arxiv.org/abs/2304.05552).
## Performance
MS COCO
| Model | Easy / Hard | Size | FLOPs | FPS | APval | APtest |
| :----------------------------------------------------------- | :---------: | :--: | :----: | :--: | :--------------: | :---------------: |
| [**Dy-YOLOv7**](https://github.com/VDIGPKU/DynamicDet/releases/download/v0.1/dy-yolov7.pt) | 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**](https://github.com/VDIGPKU/DynamicDet/releases/download/v0.1/dy-yolov7x.pt) | 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**](https://github.com/VDIGPKU/DynamicDet/releases/download/v0.1/dy-yolov7-w6.pt) | 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_trace` and `fp16`.
- More results can be found on the paper.
## Quick Start
### Installation
```bash
cd DynamicDet
conda install pytorch=1.11 cudatoolkit=11.3 torchvision -c pytorch
pip install -r requirements.txt
```
### Data preparation
Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/VDIGPKU/DynamicDet/releases/download/v0.1/coco2017labels-segments.zip).
```
├── coco
│ ├── train2017.txt
│ ├── val2017.txt
│ ├── test-dev2017.txt
│ ├── images
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── labels
│ │ ├── train2017
│ │ ├── val2017
│ ├── annotations
│ │ ├── instances_val2017.json
```
### Training
Step1: Training cascaded detector
- Single GPU training
```bash
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 🚀)
```bash
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
```bash
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-step2
```
### Getting the dynamic thresholds for variable-speed inference
```bash
python 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 val
```
### Testing
```bash
python 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
```
### Inference
```bash
python detect.py --cfg cfg/dy-yolov7-step2.yaml --weight weights/dy-yolov7.pt --num-classes 80 --source --device 0 --dy-thres
```
## Citation
If you find this repo useful in your research, please consider citing the following paper:
```BibTex
@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}
}
```
## License
The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact [email protected].