“用于对象检测的深度直接训练脉冲神经网络”的正式实现（ICCV2023）.zip

<div align="center"> <!-- <a align="center" href="https://ultralytics.com/yolov3" target="_blank"> <img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a> --> ## <div align="center">Deep Directly-Trained Spiking Neural Networks for Object Detection [(ICCV2023)](https://openaccess.thecvf.com/content/ICCV2023/html/Su_Deep_Directly-Trained_Spiking_Neural_Networks_for_Object_Detection_ICCV_2023_paper.html)</div> </div> ### Requirements 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 <b>environment.yml</b>. Some additional dependencies can be found in the <b>environment.txt</b>. <details open> <summary>Install</summary> ```bash $ git clone https://github.com/BICLab/EMS-YOLO.git $ pip install -r requirements.txt ``` </details> ### Pretrained Checkpoints We 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](https://drive.google.com/drive/folders/1mry8sdED6ncqxajmQROKBECpcrmXStpB?usp=sharing) . The relevant parameter files are in the `runs/train`. ### Training & Addition <details open> <summary>Train</summary> 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 python path/to/train_g1.py --weights ***.pt --img 640 ``` For coco dataset: ```python python train.py ``` </details> Calculating the spiking rate: Dependencies can be downloaded from [Visualizer](https://github.com/luo3300612/Visualizer). ```python python calculate_fr.py ``` ### Contact Information ```shell @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} } ``` <p> YOLOv3 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. <b>Our code is also implemented in this framework, so please remember to cite their work.</b> </p>