🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.

A paper list of spiking neural networks, including papers, codes, and related websites. 本仓库收集脉冲神经网络相关的顶会顶刊论文和代码，正在持续更新中。

🔥 This repo collects top international conference papers, codes about Spiking Neural Networks. 本仓库收集了脉冲神经网络领域的顶会顶刊论文和代码，正在持续更新中。

Offical implementation of "Spike-driven Transformer" (NeurIPS2023)

A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3(ultralytics/yolov3 & eriklindernoren/PyTorch-YOLOv3), with support for Spiking-YOLOv3-Tiny at present.

🔥🔥🔥A collection of some awesome public SNN(Spiking Neural Network) projects.

Offical implementation of "Deep Directly-Trained Spiking Neural Networks for Object Detection" (ICCV2023)

实现一种多Lora权值集成切换+Zero-Finetune零微调增强的跨模型技术方案，LLM-Base+LLM-X+Alpaca，初期，LLM-Base为Chatglm6B底座模型，LLM-X是LLAMA增强模型。该方案简易高效，目标是使此类语言模型能够低能耗广泛部署，并最终在小模型的基座上发生“智能涌现”，力图最小计算代价达成ChatGPT、GPT4、ChatRWKV等人类友好亲和效果。当前可以满足总结、提问、问答、摘要、改写、评论、扮演等各种需求。

Offical code of "QKFormer: Hierarchical Spiking Transformer using Q-K Attention" (NeurIPS 2024，Spotlight 3%)

Leaky Integrate and Fire (LIF) model implementation for FPGA

Code for VPRTempo, our temporally encoded spiking neural network for visual place recognition.

Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation

PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware

SSTDP is a efficient spiking neural network training framework, which is contributed by Fangxin Liu and Wenbo Zhao.

Implementation of the paper Keys to Accurate Feature Extraction Using Residual Spiking Neural Networks

Offical implementation of "Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training"

A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlo…

Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".

Bio-inspired neuromorphic cerebellum