Transformer

Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling

Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor Mordatch† *equal contribution, †equal advising A link to our paper can be found on arXiv. Overview Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.Contains scripts to reproduce experiments. Instructions We provide code in two sub-directories: atari containing code for Atari experiments and gym containing code for OpenAI Gym experiments.See corresponding READMEs in each folder for instructions; scripts should be run […]

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Database

A database-based CDN node supporting PostgreSQL and MongoDB backends

A database-based CDN node supporting PostgreSQL and MongoDB backends. Ubuntu host guide by Digital Ocean. A simple to use database-based deployable CDN node for hobbyist developers who wish to have their own CDN! Setup Clone this repo via this command: git clone https://github.com/justanotherbyte/imoog Go into the imoog/settings.py file and adjust your settings. Examples for both database drivers have been provided in the file. Install a production asgi server of your choice. The 2 I recommend are hypercorn and uvicorn. Installing […]

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Neural Network

The code for Deformable Neural Radiance Fields, a.k.a. Nerfies

This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. This codebase contains a re-implementation of Nerfies using JAX, building on JaxNeRF. We have been careful to match implementation details and have reproduced the original results presented in the paper. Demo We provide an easy-to-get-started demo using Google Colab! These Colabs will allow you to train a basic version of our method using Cloud TPUs (or GPUs) on Google Colab. Note that due to limited compute resources available, these […]

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Flask

Runnable Python demo of ArtLine

How to run? pip3 install -r requirements.txt python3 app.py How to use? Run the Flask app Open localhost:5000 in browser Select an image( .png or .jpg ) then click Go Art Once the process done successfully, you should see the result in browser. Enjoy and give a star if you like it. GitHub https://github.com/jwenjian/artline-demo    

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Home Assistant

Universal Xiaomi MIoT integration for Home Assistant

简体中文 | English MIoT 协议是小米智能家居从 2018 年起推行的智能设备通信协议规范，此后凡是可接入米家的设备均通过此协议进行通信。此插件按照 MIoT 协议规范与设备通信，实现对设备的状态读取及控制。 由于 MIoT 协议具有极强的通用性，已接入米家的智能设备均可通过此插件快速高效地接入 Home Assistant，而无关设备的具体型号。 本插件运行方式默认为本地接入(局域网读取/控制)，延迟极低。对于不支持本地读取的设备，支持 2 种云端接入（云端读取本地控制/云端读取云端控制）。 目前此插件已支持以下设备类型： sensor (传感器) switch (开关) cover (卷帘/晾衣架/升降帘/窗帘) light (灯，可以开关、调亮度、调色、设置灯效) fan (风扇，可以开关、设置风速、设置摇头) humidifier (加湿器/除湿器，可以开关、设置湿度、选择模式) media player (小爱音箱，可以播放/暂停/调音量，TTS/执行自定义指令二选一)(需要通过文件配置) 如果对您有帮助，欢迎给个 Star！? 如果插件工作不正常，请先参考调试部分，打开调试日志，通过日志排查问题。 如果您认为插件有 bug，或者有新功能的想法，您可以提交 Issue。 使用上的问题，请在论坛咨询，或加入 QQ 群: 982 100 289 2 月 3 日 Breaking Changes 支持了一个设备多种类型的自动配置，现在像风扇灯、晾衣架这类具有子设备的设备，可以自动识别、一次性接入。 由于 1 的原因，插件内部数据存储方式发生较大变化，部分设备需要删除重新配置，需要重新配置的设备已在通知栏中写明；文件配置的设备不受影响。 大幅提高了自动识别的准确性。 安装 将 custom_component 文件夹中的内容拷贝至自己的相应目录 或者 配置 UI 配置法 文件配置法 请参考 config_example 文件夹 内的相关文件 各个设备类型公用的配置参数： host (Required): 设备 IP。

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Detection

SWA Object Detection for python

This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA Object Detection}, author={Zhang, Haoyang and Wang, Ying and Dayoub, Feras and S{“u}nderhauf, Niko}, journal={arXiv preprint arXiv:2012.12645}, year={2020} } The full paper is available at: https://arxiv.org/abs/2012.12645. Introduction Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train your detector for an […]

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Scrape

A universal package of scraper scripts for humans

Table of Contents About The Project Getting Started Usage Contributing Sponsors License Contact Acknowledgements About The Project Scrapera is a completely Chromedriver free package that provides access to a variety of scraper scripts for most commonly used machine learning and data science domains. Scrapera directly and asynchronously scrapes from public API endpoints, thereby removing the heavy browser overhead which makes Scrapera extremely fast and robust to DOM changes. Currently, Scrapera    

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Graph

A Generalization of Transformer Networks to Graphs

Source code for the paper “A Generalization of Transformer Networks to Graphs” by Vijay Prakash Dwivedi and Xavier Bresson, at AAAI’21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI’21). We propose a generalization of transformer neural network architecture for arbitrary graphs: Graph Transformer. Compared to the Standard Transformer, the highlights of the presented architecture are: The attention mechanism is a function of neighborhood connectivity for each node in the graph. The position encoding is represented by Laplacian eigenvectors, […]

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