Motion

Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction

This repository contains the code and data for the paper “Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction” by Takuma Yagi, Md. Tasnimul Hasan and Yoichi Sato. Requirements Python 3.6+ ffmpeg numpy opencv-python pillow scikit-learn python-Levenshtein pycocotools torch (1.8.1, 1.4.0- for flow generation) torchvision (0.9.1) mllogger flownet2-pytorch Caution: This repository requires ~100GB space for testing, ~200GB space for trusted label training and ~3TB space for full training. Getting Started Download the data Download EPIC-KITCHENS-100 videos from the […]

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Self-Supervised

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models, NeurIPS 2021). Setup Create the conda environment by running : conda env create -f environment.yml Alternatively, you can update an existing conda environment by running : conda env update -f environment.yml Modify the python pathexport PYTHONPATH= Export the following variables for renderingexport MUJOCO_RENDERER=egl; export MUJOCO_GL=egl Please follow these instructions to install mujoco Bibtex If you find this code useful, please cite:

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augmentation

Facilitates implementing deep neural-network backbones, data augmentations

facilitates implementing deep neural-network backbones, data augmentations, optimizers and learning schedulers. backbones loss functions augumentation styles optimizers schedulers data types visualizations Refer to docs/installation.md for installion of general_backbone package. Model backone Currently, general_backbone supports more than 70 type of resnet models such as: resnet18, resnet34, resnet50, resnet101, resnet152, resnext50. All models is supported can be found in general_backbone.list_models() function: import general_backbone general_backbone.list_models() Results {‘resnet’: [‘resnet18’, ‘resnet18d’, ‘resnet34’, ‘resnet34d’, ‘resnet26’, ‘resnet26d’, ‘resnet26t’, ‘resnet50’, ‘resnet50d’, ‘resnet50t’, ‘resnet101’, ‘resnet101d’, ‘resnet152’, ‘resnet152d’, ‘resnet200’, ‘resnet200d’, […]

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Telegram

A Telegram Bot that can download music from Spotify

A Telegram Bot that can download music from Spotify RevSpotify is a fast, useful telegram bot to have Spotify music on Telegram. ✨ Features ( till now ) Download tracks from Spotify Download album from Spotify Download playlist from Spotify Download artist’s top tracks from Spotify ⚙️ Installation RevSpotify only and only requires Docker to run. Install Docker and start the bot, docker takes care of other dependencies. Now clone the repo: git clone https://github.com/revisto/RevSpotify cd RevSpotify Let’s take care […]

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Games

A Street Fighter game in Pygame

Street Fighter, commonly abbreviated as SF or スト, is a Japanese competitive fighting video game franchise developed and published by Capcom According to Wikipedia, I tried to create a bad clone of it using pygame 😂. Here is a glimpse of it Space.Fighter.2021-10-22.18-05-54.mp4 Installation Use the package manager pip to install pygame. Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as […]

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Python

Django Templates: Built-in Tags and Filters

Django is a powerful framework for creating web applications in Python. Its features include database models, routing URLs, authentication, user management, administrative tools, and a template language. You can compose reusable HTML that changes based on the data you pass to the template language. Django templates use tags and filters to define a mini-language that’s similar to Python—but isn’t Python. You’ll get to know Django templates through the tags and filters you use to compose reusable HTML. In this tutorial, […]

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Visualization

A sorting visualizer made with Tkinter

This is a sorting visualizer made with Tkinter. Make sure you’ve installed tkinter in your system to use this visualizer To install tkinter using python package manager, run the following: pip install tk Or follow the below website https://www.tutorialspoint.com/how-to-install-tkinter-in-python The visualizer has: Merge Sort Selection Sort Bubble Sort Insertion Sort Various colours are used to make the visualizer easily understandable. Updates are highly appreciatable GitHub View Github    

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