Wangcehao

Wangcehao's Stars

• Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB

  💎1MB lightweight face detection model (1MB轻量级人脸检测模型)

  Language:Python7.2k1.5k

• yeemachine/kalidokit

  Blendshape and kinematics calculator for Mediapipe/Tensorflow.js Face, Eyes, Pose, and Finger tracking models.

  Language:TypeScript5.3k665

• google-ai-edge/mediapipe

  Cross-platform, customizable ML solutions for live and streaming media.

  Language:C++27.4k5.1k

• deepinsight/insightface

  State-of-the-art 2D and 3D Face Analysis Project

  Language:Python23.3k5.4k

• openai/CLIP

  CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  Language:Jupyter Notebook25.7k3.3k

• CSAILVision/places365

  The Places365-CNNs for Scene Classification

  Language:Python1.9k536

• JeyDi/SeasonImageAnalyzer

  Deep neural network created with fine tuning techniques using VGG16 over Image net for Seasons recognition downloaded from flickr

  Language:Jupyter Notebook1

• hwalsuklee/tensorflow-generative-model-collections

  Collection of generative models in Tensorflow

  Language:Python3.9k858

• borisbanushev/stockpredictionai

  In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

  4.2k1.7k