Deep Learning, NLP, NMT, AI, ML
Its like rot13 but for images. Calling the algorithm imrot10 for im = image, rot = rotation, 10 = default magnitude. Unfortunately it is not symmetric like rot13 but i’m not wasting my time to develop fully symmetric so you have to use the -u parameter to undo it. python3 image-rotator.py -h usage: image-rotator.py [-h] [-i INPUT] [-o OUTPUT] [-u [UNDO]] [–magnitude MAGNITUDE] optional arguments: -h, –help show this
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A telegram bot to interact with a Minecraft Server It has the following commands: /status – Returns the server status (Online/Offline). /ip – Returns the IP of the server. /uptime – Minecraft server uptime. /list – List online players. /say – Sends an ingame message to the players. /craft – Shows the crafting recipe for a certain block/item. /version – Displays the server version. /help – This help. GitHub View Github
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How to use A simple example. – uses: beiyuouo/[email protected] with: blog_rss_link: https://blog.bj-yan.top/index.xml douban_name: 179948994 A full example. name: Build Readme on: schedule: – cron: “0 0 * * *” workflow_dispatch: push:
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browse and watch anime (scrape from gogoanime) (wip) basically ani-cli but in python cuz python good demo dependencies installation pip install git+https://github.com/sleepntsheep/ani-cli usages or notes it take quite long to open mpv, i don’t know why too 💀 GitHub View Github
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Heterogeneous Star Graph Attention Network for Product Attributes Prediction This repository contains the official PyTorch implementation for ADVIE Paper “Heterogeneous Star Graph Attention Network for Product Attributes Prediction“.We also present the Alibaba Custermers Interaction Dataset used in this paper. Citation If you find this code or dataset is helpful, please kindly consider citing the following papers: Zhao, Xuejiao, et al. “Heterogeneous star graph attention network for product attributes prediction.” Advanced Engineering Informatics. 51, 101447. 2022.
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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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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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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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