Images

OpenMMLab Image and Video Editing Toolbox in PyTorch

MMEditing Documentation actions codecov PyPI LICENSE Average time to resolve an issue Percentage of issues still open MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch works with PyTorch 1.3 to 1.6. Documentation: https://mmediting.readthedocs.io/en/latest/. Major features Modular design We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules. Support of multiple tasks in editing […]

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Video

An open-source toolbox for video understanding based on PyTorch

MMAction2 MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. The master branch works with PyTorch 1.3+.Action Recognition Results on Kinetics-400 Spatio-Temporal Action Detection Results on AVA-2.1 Skeleton-base Action Recognition Results on NTU-RGB+D-120 Major Features Modular design We decompose the video understanding framework into different components and one can easily construct a customized video understanding framework by combining different modules. Support for various datasets The toolbox directly supports multiple datasets, […]

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Visualization

Python package for the analysis and visualisation of finite-difference fields

discretisedfield Marijan Beg1,2, Martin Lang1, Ryan A. Pepper1, Thomas Kluyver1,2, and Hans Fangohr1,2,3 1 Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom2 European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany3 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany About discretisedfield is a Python package, integrated with Jupyter, providing: definition of finite-difference regions, meshes, lines, and fields, analysis of finite-difference fields, visualisation using matplotlib and k3d, and […]

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Tool

A simple project management tool for educational purposes in python

This software is used for educational and demonstrative purposes. It’s a simple project management tool powered by Django Framework Features: Class-Based Views approach Login, Sign Up, Recover (LoginView, FormView, UserCreationForm, PasswordResetForm, LogoutView, PasswordResetView, PasswordResetDoneView, PasswordResetConfirmView, PasswordResetCompleteView) Custom Extended User model (AbstractUser, BaseUserManager, UserManager) Permissions and Groups (LoginRequiredMixin, PermissionRequiredMixin) Simple CRUD views (ListView, DetailView, CreateView, UpdateView, DeleteView) File uploading Statistics (TemplateView, View, Q, F, Count, FloatField, Cast, Case, When, Sum, Avg) Forms (Form, ModelForm) Admin page (ModelAdmin, TabularInline) Template and layouts […]

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Hacking

Chromepass: Hacking Chrome Saved Passwords and Cookies

Chromepass – Hacking Chrome Saved Passwords and Cookies Chromepass is a python-based console application that generates a windows executable with the following features: Decrypt Google Chrome, Chromium, Edge, Brave, Opera and Vivaldi saved paswords and cookies Send a file with the login/password combinations and cookies remotely (http server) Undetectable by AV if done correctly Custom icon Custom error message Customize port AV Detection! The new client build methodology practically ensures a 0% detection rate, even without AV-evasion tactics. If this […]

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PyTorch

PyTorch implementation for paper Neural Marching Cubes

NMC PyTorch implementation for paper Neural Marching Cubes, Zhiqin Chen, Hao Zhang. Citation If you find our work useful in your research, please consider citing: @article{chen2021nmc, title={Neural Marching Cubes}, author={Zhiqin Chen and Hao Zhang}, journal={arXiv preprint arXiv:2106.11272}, year={2021} } Notice We have implemented Neural Dual Contouring (NDC).NDC is based on Dual Contouring and thus much easier to implement than NMC.It produces less triangles and vertices (1/8 of NMC, 1/4 of NMC-lite, ≈MC33), with better triangle quality.It runs faster than NMC […]

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Machine Learning

Large scale embeddings on a single machine

Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs requires a large amount of data movement to get embedding parameters from storage to the computational device.Marius is designed to mitigate/reduce data movement overheads using: Pipelined training and IO Partition caching and buffer-aware data orderings Details on how Marius works can be found in our OSDI ’21 Paper, where experiment scripts and configurations can be found in the […]

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