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zhfei's Projects

airsim icon airsim

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

arc-robot-vision icon arc-robot-vision

MIT-Princeton Vision Toolbox for Robotic Pick-and-Place at the Amazon Robotics Challenge 2017 - Grasp Detection and Image Matching for Novel Objects with Deep Learning

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

db-gpt icon db-gpt

Revolutionizing Database Interactions with Private LLM Technology

esvo icon esvo

This repository maintains the implementation of "Event-based Stereo Visual Odometry".

gpt-4v-act icon gpt-4v-act

AI agent using GPT-4V(ision) capable of using a mouse/keyboard to interact with web UI

grasp icon grasp

Grasping in GAZEBO robotics simulator.

huaxiaerp icon huaxiaerp

华夏ERP基于SpringBoot框架和SaaS模式,立志为中小企业提供开源好用的ERP软件,目前专注进销存+财务功能。主要模块有零售管理、采购管理、销售管理、仓库管理、财务管理、报表查询、系统管理等。支持预付款、收入支出、仓库调拨、组装拆卸、订单等特色功能。拥有库存状况、出入库统计等报表。同时对角色和权限进行了细致全面控制,精确到每个按钮和菜单。

iot-master icon iot-master

物联大师是开源免费的物联网中台系统,集成了标准Modbus、电力、水务和主流PLC等多种协议,支持数据采集、公式计算、定时控制、自动控制、异常报警、流量监控、Web组态、远程调试等功能,适用于大部分物联网和工业互联网应用场景。

langchain icon langchain

⚡ Building applications with LLMs through composability ⚡

monorec icon monorec

Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)

paddleocr icon paddleocr

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

pythonrobotics-cn icon pythonrobotics-cn

机器人算法的 Python 示例代码Python sample codes for robotics algorithms.

r3live icon r3live

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

robot-grasp-detection icon robot-grasp-detection

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.

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