Popular GitHub Repositories for Ad Filter

ECCV2022 - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.

Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors

???色情图片离线识别，基于TensorFlow实现。识别只需20ms,可断网测试，成功率99%，调用只要一行代码，从雅虎的开源项目open_nsfw移植，该模型文件可用于iOS、java、C++等平台

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

A free, open source, and privacy-focused browser extension to block “not safe for work” content built using TypeScript and TensorFlow.js.

High-performance automatic differentiation of LLVM and MLIR.

A library created to revitalize C++ as a machine learning front end. Per aspera ad astra.

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

State Space Models library in JAX

哔哩哔哩浏览器插件：基于大语言模型，对B站视频中的植入广告进行检测。一键跳过视频中的植入/口播广告。

Building Convolutional Neural Networks From Scratch using NumPy

iOS Core ML implementation of waifu2x

Computer vision based vehicle detection and tracking using Tensorflow Object Detection API and Kalman-filtering

[NeurIPS 2021] [T-PAMI] Global Filter Networks for Image Classification

[NeurIPS Workshop 2019] Official code of the paper "Probabilistic 3D Multi-Object Tracking for Autonomous Driving." First Place of the First NuScenes Tracking Challenge in the AI Driving Olympics Workshop of NeurIPS.

Computer Vision Practice

If you can measure it, consider it predicted

Porn images detector with python, tensorflow, scikit-learn and opencv.

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".