twl_spike_yolo

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.

<p align="center"> <img src="https://huggingface.co/KirillHit/twl_spike_yolo/resolve/main/assets/gen1_example.gif" alt="Demo GIF"/><br> <em>Demonstration of model performance on the Gen1 dataset</em> </p>

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

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