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zhangjf2018's Projects

3ddfa_v2 icon 3ddfa_v2

The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.

ace-cache icon ace-cache

基于spring boot上的注解缓存,自带轻量级缓存管理页面。@Cache比spring cache更轻量的缓存,支持单个缓存设置过期时间,可以根据前缀移除缓存。采用fastjson序列化与反序列化,以json串存于缓存之中。ace-cache可以快速用于日常的spring boot应用之中。

admin icon admin

Java EE 企业级快速开发平台,基于 Spring Boot、Spring MVC、Shiro、Flyway、Beetl、BeetlSQL、Bootstrap、AdminLTE 在线代码生成功能,采用经典开发模式,让初学者能够更快的入门并投入到团队开发中去。包括核心模块如:组织机构、角色用户、菜单及按钮授权、数据权限、系统参数、内容管理、工作流等。松耦合设计;界面无刷新;一键换肤;众多账号安全设置,密码策略;在线定时任务配置;支持 SAAS 多租户;支持集群部署;支持多数据源

ai icon ai

artificial intelligence on python!

anet2016-cuhk icon anet2016-cuhk

Action Recognition Toolbox for CUHK&ETHZ&SIAT submission to ActivityNet 2016

banner icon banner

Android广告图片轮播控件,支持无限循环和多种主题,可以灵活设置轮播样式、动画、轮播和切换时间、位置、图片加载框架等!

bitcoin icon bitcoin

Bitcoin Core integration/staging tree

blind_watermark icon blind_watermark

Blind/Invisible Watermark (图片盲水印,提取水印无须原图!)

bolt icon bolt

Bolt is a deep learning library with high performance and heterogeneous flexibility.

book icon book

Deep Learning 101 with PaddlePaddle (『飞桨』深度学习框架入门教程)

canal icon canal

阿里巴巴mysql数据库binlog的增量订阅&消费组件 。阿里云DRDS( https://www.aliyun.com/product/drds )、阿里巴巴TDDL 二级索引、小表复制powerd by canal. Aliyun Data Lake Analytics https://www.aliyun.com/product/datalakeanalytics powered by canal

cascadetabnet icon cascadetabnet

This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"

ccpd icon ccpd

[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition

centernet icon centernet

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

centernet-deep-sort icon centernet-deep-sort

realtime multiple people tracking (centerNet based person detector + deep sort algorithm with pytorch)

chineseocr_lite icon chineseocr_lite

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

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