Update README.md

README.md

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----
-license: mit
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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.
+
+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)
+
+---