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

Driven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution in the field, greatly extending the model's receptive field and achieving significant performance improvements. However, this improvement comes at a cost, as the quadratic complexity of the self-attentive mechanism increases the computational burden of the model. To address this problem, we introduce a simple yet effective baseline approach called Mamba YOLO. Our contributions are as follows: 1) We propose that the ODMamba backbone introduce a State Space Model (SSM) with linear complexity to…

• NN
  National Natural Science Foundation of China

  Award: 62376252
• NS
  Natural Science Foundation of Zhejiang Province

  Award: LZ22F030003