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---
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.
<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](https://github.com/KirillHit/twl_spike_yolo)
--- |