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cainiaopcw's Projects

2019-ccf-bdci-ocr-mczj-ocr-identificationidelement

2019CCF-BDCI大赛最佳创新探索奖获得者基于OCR身份证要素提取赛题冠军天晨破晓团队赛题源码

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/自然语言处理(NLP)/深度学习(Deep Learning)/机器学习(Machine Learning)/C/C++/Python/面试笔记，此外，还包括创建者看到的所有机器学习/深度学习面经中的问题。除了其中 DL/ML 相关的，其他与算法岗相关的计算机知识也会记录。但是不会包括如前端/测试/JAVA/Android等岗位中有关的问题。

background-matting

Background Matting: The World is Your Green Screen

bert-chinese-text-classification-pytorch

使用Bert，ERNIE，进行中文文本分类

centernet

Object detection, 3D detection, and pose estimation using center point detection:

chinese-text-classification-pytorch

中文文本分类，TextCNN，TextRNN，FastText，TextRCNN，BiLSTM_Attention，DPCNN，Transformer，基于pytorch，开箱即用。

chineseaddress_ocr

Photographing Chinese-Address OCR implemented using CTPN+CTC+Address Correction. 拍照文档中文地址文字识别。

chineseocr_lite

超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

darknet

YOLOv4v / Scaled-YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

db

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

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

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。

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

gaussian_yolov3

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving (ICCV, 2019)

hyperlpr

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

hypervid

开源移动端车型识别 Mobile Plateform Vehicle Identification Model

lets_ocr

A repository for OCR, which inlcudes some classical OCR algorithms Pytorch implementation such as CTPN, EAST and CRNN.

Light-LPR is an open source project aimed at license plate recognition that can run on embedded devices, mobile phones, and x86 platforms. It aims to support license plate recognition in various scenarios. The accuracy rate of license plate character recognition exceeds 99.95%, and the comprehensive recognition accuracy rate exceeds 99.%, Support multi-country and multilingual license plate recognition.

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