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3d-resnets-pytorch

3D ResNets for Action Recognition (CVPR 2018)

action-recognition

Action Recongition Using STIP and BoW/SVM pipeline

autotest

awesome-face_recognition

papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

banksystem

java课设

beam-site-zh

Apache Beam 官方网站中文版

buybook

spring的jdbc练习

c3d-tensorflow

C3D is a modified version of BVLC tensorflow to support 3D ConvNets.

craft-pytorch

Official implementation of Character Region Awareness for Text Detection (CRAFT)

datasketch

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble

db

A PyToch implementation of "Real-time Scene Text Detection with Differentiable Binarization".

deep-text-recognition-benchmark

Text recognition (optical character recognition) with deep learning methods.

faster_rcnn_pytorch

Faster RCNN with PyTorch

hyperlpr

基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.

javalearning

Java

label-noise-papers

An update-to-date list for papers related with label-noise representation learning is here.

mmaction

An open-source toolbox for action understanding based on PyTorch

modelarts-lab

ModelArts-Lab是示例代码库。更多AI开发学习交流信息，请访问华为云AI开发者社区：https://developer.huaweicloud.com/techfield/ai.html

models

Models and examples built with TensorFlow

networkemergency

网络应急响应平台

opencv

Open Source Computer Vision Library

scanner

二维码/条码识别、身份证识别、银行卡识别、车牌识别、图片文字识别、黄图识别、驾驶证（驾照）识别

sniper

SNIPER is an efficient multi-scale object detection algorithm

tsn-pytorch

Temporal Segment Networks (TSN) in PyTorch

two-stream-action-recognition

Using two stream architecture to implement a classic action recognition method on UCF101 dataset

twostreamfusion

Code release for "Convolutional Two-Stream Network Fusion for Video Action Recognition", CVPR 2016.