metadata

language:
  - en
  - ru
license: mit
library_name: other
tags:
  - spiking-neural-network
  - object-detection
  - event-based
  - yolo
  - neuromorphic
pipeline_tag: object-detection

TWL Spike Yolo

TWL Spike Yolo is a spiking neural network (SNN) for real-time object detection based on event-based vision. The model adapts the YOLOv8 architecture to work with streams of event data, allowing efficient processing in neuromorphic computing environments.

This approach leverages the low-latency and power-efficient properties of SNNs to detect objects in fast-changing visual scenes. The model also explores multimodal fusion by combining event-based and frame-based inputs to enhance detection accuracy under challenging conditions such as motion blur or low light.

Highlights

Architecture: YOLOv8-inspired spiking neural network.
Input: Event data from neuromorphic (event-based) cameras; optionally combined with standard image frames.
Use case: High-speed, low-latency object detection with improved energy efficiency.
Applications: Robotics, autonomous driving, surveillance, and edge devices using neuromorphic hardware.

Source Code

The implementation, training scripts, and inference tools are available in the GitHub repository:
👉 https://github.com/KirillHit/twl_spike_yolo