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

Zeyu Wang, Chen Li, Huiying Xu, Xinzhong Zhu, Hongbo Li

Model Zoo

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

Inference on MSCOCO2017 dataset

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.

@inproceedings{wang2025mamba,
  title={Mamba yolo: A simple baseline for object detection with state space model},
  author={Wang, Zeyu and Li, Chen and Xu, Huiying and Zhu, Xinzhong and Li, Hongbo},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={39},
  number={8},
  pages={8205--8213},
  year={2025}
}