webwarz's starred repositories
Langchain-Chatchat
Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
KnowledgeGraph
knowledge graph知识图谱,从零开始构建知识图谱
review_object_detection_metrics
Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.
Object-Detection-Metrics
Most popular metrics used to evaluate object detection algorithms.
Weak_Class_Source_Separation
This repository contains supplementary material for the paper: "Audio Source Separation Using Variational Autoencoders and Weak Class Supervision"
Dual-Path-RNN-Pytorch
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
Sound_Localization_Algorithms
Classical algorithms of sound source localization with beamforming, TDOA and high-resolution spectral estimation.
AudioClassification-Tensorflow
基于Tensorflow实现声音分类，博客地址：
Phase-aware-Deep-Complex-UNet
[Not Official] Implementation DC-UNet, ICLR 2019
SpeechAlgorithms
Speech Algorithms
deep_complex_networks
Implementation related to the Deep Complex Networks
MobileIMSDK
一个原创多端IM通信层框架，轻量级、高度提炼，历经10年、久经考验。可能是市面上唯一同时支持UDP+TCP+WebSocket三种协议的同类开源框架，支持 iOS、Android、Java、H5、小程序、Uniapp，服务端基于Netty。
Shift-AI-models-to-real-world-products
Share some useful guides and references about how to shift AI models to real world products or projects.
nlp_paper_study
该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记
ML-KWS-for-MCU
Keyword spotting on Arm Cortex-M Microcontrollers
TensorFlow-2.x-Tutorials
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。
Deep-Learning-with-TensorFlow-book
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.