brfIMU

brfIMU's Stars

• llhthinker/MachineLearningLab

  Some experiments about Machine Learning

  Language:Python116191

• wanghaao/ImpAPTr

  A Tool For Identifying The Clues To Online Service Anomalies

  Language:Python106

• dianping/cat

  CAT 作为服务端项目基础组件，提供了 Java, C/C++, Node.js, Python, Go 等多语言客户端，已经在美团点评的基础架构中间件框架（MVC框架，RPC框架，数据库框架，缓存框架等，消息队列，配置系统等）深度集成，为美团点评各业务线提供系统丰富的性能指标、健康状况、实时告警等。

  Language:Java18.7k5.4k

• NetManAIOps/Squeeze

  ISSRE 2019: Generic and Robust Localization of Multi-Dimensional Root Cause

  Language:Python9819

• kengerlwl/AprioriAlgorithm

  我的毕业设计，主要是基于关联算法的教学数据分析。具体涉及到原生经典的剪枝Apriori算法，CBE 二进制编码优化的Apriori算分，FP-Growth树算法，以及我个人基于CBE的动态采用算法

  Language:Jupyter Notebook81

• NetManAIOps/PSqueeze

  Language:Python343

• godelshana/aiops24-RAG

  用于AIOPS24挑战赛的代码库

  Language:Shell1

• shaido987/riskloc

  Implementation of RiskLoc, a method for localizing multi-dimensional root causes.

  Language:Python12221

• Feaxure-fresh/TL-Fault-Diagnosis-Library

  Fault Diagnosis Employing Transfer Learning Techniques: Domain Adaptation and Domain Generalization

  Language:Python36740

• NetManAIOps/DejaVu

  Code and datasets for FSE'22 paper "Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems"

  Language:Jupyter Notebook7416

• LeeXuanHua/Tsinghua-Advanced-Network-Management-Assignment-2

  Data analysis assignment (to analyze root cause and propagation of failures in microservices system) by Tsinghua for Advanced Network Management module Spring 2023

  Language:HTML1

• vvlj/rcl

  rcl(Root cause localization)根因定位算法设计，是2020年**软件杯题目--基于网络拓扑及告警的故障根因定位系统实现及算法研究，网址http://www.cnsoftbei.com/plus/view.php?aid=479

  Language:Python102

• lalaland1921/Squeeze

  多维监控异常根因分析，复现论文ISSRE 2019 REG paper 'Generic and Robust Localization of Multi-Dimensional Root Cause'.

  Language:Python5110

• M-panahandeh/CARE-Context-Aware-Root-Cause-Identification-Using-Distributed-Traces-and-Profiling-Metrics

  Root cause localization approch

  Language:Python1

• layumi/Person_reID_baseline_pytorch

  :bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

  Language:Python4.2k1k

• WongKinYiu/yolov9

  Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

  Language:Python9.1k1.5k

• vsai-git/YOLOv8-GUI

  a GUI application, which uses YOLOv8 for Object Detection/Tracking, Human Pose Estimation/Tracking from images, videos or camera

  101

• ghlerrix/DataAnalyze

  Object-detection dataset analyze

  Language:Python274

• monkeyDemon/AI-Toolbox

  Algorithm Engineer Toolbox, for the purpose of quickly iterating new ideas

  Language:Python413202

• vranlee/CMFTNet

  CMFTNet: Multiple Fish Tracking Based on Counterpoised JointNet - Official Implementation

  Language:Python81

• carryai/yolov8_tracking

  Real-time multi-object, segmentation and pose tracking using YOLOv8 with DeepOCSORT and LightMBN

  Language:Python1

• BrianLangat/Yolov5_StrongSORT_OSNet_new

  Language:Python2

• jingh-ai/YOLOv8-DeepSort-PyQt-GUI

  a GUI application, which uses YOLOv8 for Object Detection/Tracking, Human Pose Estimation/Tracking from images, videos or camera

  Language:Python25448

• hafizddev/YOLOv8-DeepSort-PyQt-GUI

  a GUI application, which uses YOLOv8 for Object Detection/Tracking, Human Pose Estimation/Tracking from images, videos or camera

  1

• Jai-wei/YOLOv8-PySide6-GUI

  YoloSide - YOLOv8 GUI By PySide6

  Language:Python980149

• damo-cv/TransReID

  [ICCV-2021] TransReID: Transformer-based Object Re-Identification

  Language:Python834178

• rainofmine/Repulsion_Loss

  Pytorch implementation of Repulsion Loss on RetinaNet

  Language:Python9521

• Amnx404/yolov8_tracking

  Real-time multi-object tracking and segmentation using YOLOv8 with DeepOCSORT and OSNet

  Language:Python1

• lixiangwang/MGN-pytorch

  Reproduction of paper: Learning Discriminative Features with Multiple Granularities for Person Re-Identification

  219

• khalidw/MOT16_Annotator

  This tool takes video as an input and allows the user to create bounding boxes on each frame, these bounding boxes are saved to a gt.txt file in MOT16 format. The purpose of this file is to allow the user to manually create ground truth for their custom dataset. This ground truth file can then be used in conjunction with tracker output file to generate MOT metrics to gauge the performance of tracker on custom data.

  Language:Python135