augmentation

Adversarial Differentiable Data Augmentation for Autonomous Systems

This repository provides the official PyTorch implementation of the ICRA 2021 paper: Adversarial Differentiable Data Augmentation for Autonomous SystemsAuthor: Manli Shu, Yu Shen, Ming C Lin, Tom Goldstein Environment The code has been tested on: python == 3.7.9 pytorch == 1.10.0 torchvision == 0.8.2 kornia == 0.6.2More dependencies can be found at ./requirements.txt Hardware requirements: The default training and testing setting requires 1 GPU. Data Datasets appeared in our paper can be downloaded/generated by following the directions in this page. […]

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Scripts

Haphazard scripts for scraping bitcoin/bitcoin data from GitHub

This is a quick-and-dirty tool used to scrape bitcoin/bitcoin pull request andcommentary data. Each output/ folder contains comments.json: an aggregated list of both issue and review comments, in Github’soriginal format commits.json: a list of commit objects corresponding to the PR, in Github’soriginal format pr.json: the pull request object, in Github’s original format comments_abbrev.csv: abbreviated representation of each comment in CSV format pr_abbrev.csv: abbreviated representation of the PR in CSV format done: the datetime we retrieved the PR data Limitations Right […]

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Challenge

Capsule endoscopy detection DACON challenge

Overview Yolov5, Yolor, mmdetection기반의 모델을 사용 (총 11개 모델 앙상블) 모든 모델은 학습 시 Pretrained Weight을 yolov5, yolor, mmdetection 및 swin transformer github로부터 받아서 사용 각 방식에 필요한 형태로 데이터의 format 변경 Train set과 Validation set을 나누어 진행 총 11개의 결과를 앙상블 detectors_casacde_rcnn_resnet50_multiscale, retinanet_swin-l, retinanet_swin-l_multiscale, retinanet_swin-t, atss_swin-l_multiscale, faster_rcnn-swin-l_multiscale, yolor_tta_multiscale, yolov5x, yolov5x_tta, yolov5x_tta_multiscale Weighted boxes fusion (WBF) 방식으로 앙상블 진행 (Iou threshold = 0.4) 모델에 관한 보다 자세한 내용은 /all_steps 폴더 내에 STEP2로 시작하는 .sh 스크립트들에 적힌 주석을 참고해주세요! 환경(env) 세팅 […]

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Runtime

Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022

Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed using Python 3.6.9 on Ubuntu 18.04 LTS. Name Version Docker 20.10.8 Docker Compose 1.23.2 Python Package Knowledge Graph We have opened our knowledge graphs in releases. If you need to create a new knowledge graph, follow the instructions below: First, you need to install Neo4j 4.1.1 and its required Java version (Java SE 11). Install extra Python dependencies: pip install -r build_KG/requirements.txt […]

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Machine Learning

Visual Adversarial Imitation Learning using Variational Models (VMAIL)

This is the official implementation of the NeurIPS 2021 paper. Method VMAIL simultaneously learns a variational dynamics model and trains an on-policyadversarial imitation learning algorithm in the latent space using only model-basedrollouts. This allows for stable and sample efficient training, as well as zero-shotimitation learning by transfering the learned dynamics model Instructions Get dependencies: conda env create -f vmail.yml conda activate vmail cd robel_claw/robel pip install -e . To train agents for each environmnet download the expert data from the […]

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Fetching

Fetching Cryptocurrency Prices from Coingecko and Displaying them on Grafana

Fetching Cryptocurrency Prices from Coingecko and Displaying them on Grafana About This stack consists of: Prometheus (timeseries database) Pushgateway (introduces a /metrics endpoint for prometheus to scrape) Grafana (the best) Redis (k/v in memory store) Python Script that pulls crypto market data, publishes to pushgateway for prometheus to scrape, and stores market data in Redis Flask API to fetch market prices for other projects I’m using coingecko’s api which gives me 50 free calls per minute. Walkthrough Clone the repo: […]

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CMS

A student information management system in Python

本项目是一个学生信息管理系统，这个项目是用Python语言实现的，也实现了图形化界面的显示，同时也实现了管理员端，学生端两个登陆入口，同时底层使用的是Redis做的数据持久化。This project is a student information management system, this project is implemented in Python language, but also to achieve the graphical interface display, but also to achieve the administrator side, the student side of the two login entrance, at the same time the bottom using Redis to do data persistence. Python实现学生信息管理系统图形化界面-老师端-学生端项目 一，本项目简介 1.1 项目的功能介绍： 本项目是一个有关学生信息管理系统的项目，项目实现语言：Python。相关的功能： 常见的学生信息的增删改查操作。 当查询单个学生的信息时可以在界面上显示学生的一寸免冠照。 可以区分期中，期末的成绩。 后台使用的是Redis数据库 实现了教师端登陆，学生端登录双页面。 可以进行用户密码的修改 1.2 项目涉及到的技术点： Python语言实现相关的逻辑 tkinter页面设计，图片处理等相关的Python库 Python对接Redis数据库实现数据的相关操作 二，项目结构，环境及展示 2.1 项目结构 当前项目 img（存储相关图片资源的目录） InitData.py（为数据库初始化学生数据使用，用于做实验） Main.py（运行项目的入口文件） LoginPage.py（实现老师，学生登录的功能实现文件） StudentManager.py（老师端功能实现页面） StudentOnly.py（学生端功能实现页面） 2.2 项目环境 2.2.1 Python环境 […]

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