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[AAAI2025] Mamba YOLO: A Simple Baseline for Object Detection with State Space Model

Model Zoo

We've pre-trained YOLO-World-T/M/L from scratch and evaluate on the MSCOCO2017 val.

Inference on MSCOCO2017 dataset

model	Params	FLOPs	${AP}^{val}$	${AP}_{{50}}^{val}$	${AP}_{{75}}^{val}$	${AP}_{{S}}^{val}$	${AP}_{{M}}^{val}$	${AP}_{{L}}^{val}$
Mamba YOLO-T	5.8M	13.2G	44.5	61.2	48.2	24.7	48.8	62.0
Mamba YOLO-M	19.1M	45.4G	49.1	66.5	53.5	30.6	54.0	66.4
Mamba YOLO-L	57.6M	156.2G	52.1	69.8	56.5	34.1	57.3	68.1

Getting started

1. Installation

Mamba YOLO is developed based on torch==2.3.0 pytorch-cuda==12.1 and CUDA Version==12.6.

2.Clone Project

git clone https://github.com/HZAI-ZJNU/Mamba-YOLO.git

3.Create and activate a conda environment.

conda create -n mambayolo -y python=3.11
conda activate mambayolo

4. Install torch

pip3 install torch===2.3.0 torchvision torchaudio

5. Install Dependencies

pip install seaborn thop timm einops
cd selective_scan && pip install . && cd ..
pip install -v -e .

6. Prepare MSCOCO2017 Dataset

Make sure your dataset structure as follows:

├── coco
│   ├── annotations
│   │   ├── instances_train2017.json
│   │   └── instances_val2017.json
│   ├── images
│   │   ├── train2017
│   │   └── val2017
│   ├── labels
│   │   ├── train2017
│   │   ├── val2017

7. Training Mamba-YOLO-T

python mbyolo_train.py --task train --data ultralytics/cfg/datasets/coco.yaml \
 --config ultralytics/cfg/models/mamba-yolo/Mamba-YOLO-T.yaml \
--amp  --project ./output_dir/mscoco --name mambayolo_n

Acknowledgement

This repo is modified from open source real-time object detection codebase Ultralytics. The selective-scan from VMamba.

Citations

If you find Mamba-YOLO is useful in your research or applications, please consider giving us a star 🌟 and citing it.

@misc{wang2024mambayolossmsbasedyolo,
      title={Mamba YOLO: SSMs-Based YOLO For Object Detection}, 
      author={Zeyu Wang and Chen Li and Huiying Xu and Xinzhong Zhu},
      year={2024},
      eprint={2406.05835},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.05835}, 
}