Pinned Repositories
ASRFrame
An Automatic Speech Recognition Frame ,一个中文语音识别的完整框架, 提供了多个模型
PixPro
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021
VI_ORB_SLAM2
Monocular/Stereo Visual-Inertial ORB-SLAM based on ORB-SLAM2
Yolo-Fastest
:zap: Yolo universal target detection model combined with EfficientNet-lite, the calculation amount is only 230Mflops(0.23Bflops), and the model size is 1.3MB
2021-CV-Surveys
2021 年,计算机视觉相关综述。包括目标检测、跟踪........
ACTION-Net
acuity-models
Acuity Model Zoo
AdapNet
TensorFlow model for training AdapNet for semantic segmentation
AI-Benchmark
Build keras models for 9 Tasks in AI-Benchmark: Object Detection: Mobile-v2, Inception-v3, Face Recognition: Inception-Resnet-v1, Super Resolution: SRCNN, VDSR, SRGAN, Resnet-12+GAN, and Semantic Segmantation: ICNet
AiBenchmark
Tool to test the AI capability of hardware
baodijun's Repositories
baodijun/jsoncpp
A C++ library for interacting with JSON.
baodijun/awesome-LLMs-In-China
**大模型
baodijun/Modern-CMake-zh_CN
CMake 教程 Modern-CMake 的简体中文翻译,中文版 Gitbook :https://modern-cmake-cn.github.io/Modern-CMake-zh_CN/ Chinese(simplified) translation of famous cmake tutorial Modern CMake. GitHub Pages : https://modern-cmake-cn.github.io/Modern-CMake-zh_CN/
baodijun/stb
stb single-file public domain libraries for C/C++
baodijun/ELSED
ELSED: Enhanced Line SEgment Drawing
baodijun/aliyun-oss-cpp-sdk
Aliyun OSS SDK for C++
baodijun/ulog
lightweight logging for embedded microcontrollers
baodijun/Python
All Algorithms implemented in Python
baodijun/Simple_OS_For_Studing
本项目是我自己编写一个简单操作系统的过程,大伙可以通过此项目来深入了解操作系统层次方面的细节,这为从事系统底层工作有一定的帮助
baodijun/EasyLogger
An ultra-lightweight(ROM<1.6K, RAM<0.3k), high-performance C/C++ log library. | 一款超轻量级(ROM<1.6K, RAM<0.3k)、高性能的 C/C++ 日志库
baodijun/eat_pytorch_in_20_days
Pytorch🍊🍉 is delicious, just eat it! 😋😋
baodijun/FULiveDemoDroid
Faceunity 面部跟踪和虚拟道具 SDK 在 Android 平台中的集成 Demo
baodijun/tensorflow
An Open Source Machine Learning Framework for Everyone
baodijun/memory-leak-detector
baodijun/ios-cmake
A CMake toolchain file for iOS, macOS, watchOS & tvOS C/C++/Obj-C++ development
baodijun/jadx
Dex to Java decompiler
baodijun/os-tutorial-cn
从零开始编写一个操作系统教程 -- 中文版
baodijun/coremark
CoreMark® is an industry-standard benchmark that measures the performance of central processing units (CPU) and embedded microcrontrollers (MCU).
baodijun/pybind11
Seamless operability between C++11 and Python
baodijun/CvPytorch
CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.
baodijun/threadpool
based on C++11 , a mini threadpool , accept variable number of parameters 基于C++11的线程池,简洁且可以带任意多的参数
baodijun/Assembly
⚡ 亲手编写实现基于王爽老师《汇编语言》的300个汇编程序例程 | Implementation of 300 assembly program examples based on "Assembly Language"
baodijun/pybind11-Chinese-docs
pybind11中文文档(个人翻译)
baodijun/ffmpeg-video-player
An FFmpeg and SDL Tutorial.
baodijun/CNStream
CNStream is a streaming framework for building Cambricon machine learning pipelines http://forum.cambricon.com https://gitee.com/SolutionSDK/CNStream
baodijun/marktext
📝A simple and elegant markdown editor, available for Linux, macOS and Windows.
baodijun/log4cplus
log4cplus is a simple to use C++ logging API providing thread-safe, flexible, and arbitrarily granular control over log management and configuration. It is modelled after the Java log4j API.
baodijun/ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform
baodijun/SPACH
baodijun/TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.