This project is a combined neural network utilizing an spiking CNN with backpropagation and YOLOv3 for object detection.
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.6. In addition you will need to have setup Jupyter with CUDA support for GPU.
Download the dataset N-Caltech101 from https://www.garrickorchard.com/datasets/n-caltech101 and unzip in the project root.
With Jupyter, run data_processing.ipynb. This might take a while, you might not need to process all data.
The full network can be run through spiking_yolov3.ipynb while only the original YOLOv3 is runnable through original_yolov3.ipynb.
Code for spiking solution is modified from https://github.com/yjwu17/BP-for-SpikingNN and can be found in the directory SpikingNN.
Most of main YOLOv3 code can be found in train.py which is originally from https://github.com/ultralytics/yolov3