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C++ matlab python HTML5

opencv caffe pytorch tensorflow

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Sida DAI's Projects

detectron2 icon detectron2

Detectron2 is FAIR's next-generation platform for object detection and segmentation.

dive-into-dl-pytorch icon dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

dl_learning_notes icon dl_learning_notes

my records, codes and articles and issue i met when i learn the deep learning framework tensor-flow.

doc icon doc

开发过程中,自己的一些记录,喜欢请拿走。(Deprecated)

dockerbook-code icon dockerbook-code

The code and configuration examples from The Docker Book (http://www.dockerbook.com)

docsify icon docsify

🃏 A magical documentation site generator.

echarts icon echarts

Apache ECharts is a powerful, interactive charting and data visualization library for browser

eco-pytorch icon eco-pytorch

PyTorch implementation for "ECO: Efficient Convolutional Network for Online Video Understanding", ECCV 2018

efgrnet icon efgrnet

Enriched Feature Guided Refinement Network for Detection,ICCV2019.

facedet icon facedet

实现常用基于深度学习的人脸检测算法

faceit icon faceit

A script to make it easy to swap faces in videos using the faceswap library, and YouTube videos.

facekit icon facekit

Implementations of PCN (an accurate real-time rotation-invariant face detector) and other face-related algorithms

facenet_pytorch icon facenet_pytorch

PyTorch implementation of the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering"

faceswap icon faceswap

https://www.zhihu.com/question/264019472 Non official project based on original /r/Deepfakes thread. Many thanks to him!

faceswap-1 icon faceswap-1

Real-time FaceSwap application built with OpenCV and dlib

faceswap-2 icon faceswap-2

Python script to put facial features from one face onto another

faceswap-gan icon faceswap-gan

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

fashion-mnist icon fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

fastapi icon fastapi

FastAPI framework, high performance, easy to learn, fast to code, ready for production

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