Deep Directly-Trained Spiking Neural Networks for Object Detection (ICCV2023)
The code has been tested with pytorch=1.10.1,py=3.8, cuda=11.3, cudnn=8.2.0_0 . The conda environment can be copied directly via environment.yml. Some additional dependencies can be found in the environment.txt.
Install
$ git clone https://github.com/BICLab/EMS-YOLO.git
$ pip install -r requirements.txtWe provide the best and the last trained model based on EMS-Res34 on the COCO dataset.
detect.py runs inference on a variety of sources, downloading models automatically from
the COCO_EMS-ResNet34 .
The relevant parameter files are in the runs/train.
Train
The relevant code for the Gen1 dataset is at /g1-resnet. It needs to be replaced or added to the appropriate root folder.
For gen1 dataset:
python path/to/train_g1.py --weights ***.pt --img 640For coco dataset:
python train.pyCalculating the spiking rate:
Dependencies can be downloaded from Visualizer.
python calculate_fr.py@inproceedings{su2023deep,
title={Deep Directly-Trained Spiking Neural Networks for Object Detection},
author={Su, Qiaoyi and Chou, Yuhong and Hu, Yifan and Li, Jianing and Mei, Shijie and Zhang, Ziyang and Li, Guoqi},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={6555--6565},
year={2023}
}YOLOv3 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
Our code is also implemented in this framework, so please remember to cite their work.
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