This is the official MegEngine implementation of MAF-YOLO, from the following PRCV2024 paper:
Article Interpretation: 集智书童
MS COCO
| Model | Test Size | #Params | FLOPs | APval | AP50val | APtest2017 | AP50test2017 | 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 | APval | AP50val | APtest2017 | AP50test2017 | 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 |
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
# 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_evalSingle 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 0Multiple 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 0,1,2,3Dataset 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"] 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}
}