MAF-YOLO

This is the official MegEngine implementation of MAF-YOLO, from the following PRCV2024 paper:

Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection.

Article Interpretation: 集智书童

Performance

MS COCO

Model	Test Size	#Params	FLOPs	AP^val	AP₅₀^val	AP^test2017	AP₅₀^test2017	epoch
MAF-YOLO-N	640	3.8M	10.5G	42.4%	58.9%	42.1%	58.6%	300
MAF-YOLO-S	640	8.6M	25.5G	47.4%	64.3%	47.2%	64.0%	300
MAF-YOLO-M	640	23.7M	76.7G	51.2%	68.5%	50.9%	68.1%	300

Our second version of the work to improve on YOLOv10 has yielded preliminary results that compare favorably with both YOLOv9 and YOLOv10, which will be made public in the future!

Model	Test Size	#Params	FLOPs	AP^val	AP₅₀^val	AP^test2017	AP₅₀^test2017	epoch
YOLOv10n	640	2.3M	6.7G	38.5%	53.8%	38.7%	54.4%	500
MAF-YOLOv10n	640	2.2M	7.2G	42.3%	58.5%	42.3%	58.4%	500
YOLOv10s	640	7.2M	21.6G	46.3%	63.0%	45.9%	62.4%	500
MAF-YOLOv10s	640	7.1M	25.3G	48.9%	65.9%	48.8%	65.5%	500
YOLOv10m	640	15.4M	59.1G	51.1%	68.1%	51.2%	68.0%	500
MAF-YOLOv10m	640	15.3M	65.2G	52.7%	69.5%	-	-	500

Installation

conda create -n mafyolo python==3.8
conda activate mafyolo
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

Evaluation

# evaluate MAF-YOLOn
python tools/eval.py --weights MAFYOLOn.pt --data data/coco.yaml

# evaluate MAF-YOLOs
python tools/eval.py --weights MAFYOLOs.pt --data data/coco.yaml --reproduce_640_eval

# evaluate MAF-YOLOm
python tools/eval.py --weights MAFYOLOm.pt --data data/coco.yaml --reproduce_640_eval

Train

Single GPU training

# Loading pre-trained weight to train MAFYOLOn
python tools/train.py --conf configs/pretrain/MAF-YOLO-n-pretrain.py --data data/coco.yaml --device 0

# Training MAFYOLOn from scratch
python tools/train.py --conf configs/MAF-YOLO-n.py --data data/coco.yaml --device 0

Multiple GPU training

# Training MAFYOLOn from scratch with multiple GPU
python -m torch.distributed.run --nproc_per_node 4 --master_port 9527 python tools/train.py --conf configs/MAF-YOLO-n.py --data data/coco.yaml --device 0,1,2,3

Dataset Configuration

Dataset file structure

├── data
│   ├── images
│   │   ├── train
│   │   └── val
│   ├── labels
│   │   ├── train
│   │   ├── val

data.yaml

train: data/images/train 
val: data/images/val 
is_coco: False
nc: 3  
names: ["car","person","bike"]

Citation

If our code or model is helpful to your work, please cite our paper. We would be very grateful!

@article{yang2024multi,
  title={Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection},
  author={Yang, Zhiqiang and Guan, Qiu and Zhao, Keer and Yang, Jianmin and Xu, Xinli and Long, Haixia and Tang, Ying},
  journal={arXiv preprint arXiv:2407.04381},
  year={2024}
}

yang-0201/MAF-YOLO