Telegram

A telegram bot to monitor the latest NFT price on BSC

This is a telegram bot for monitoring price and ranking of NFT on Binance Smart Chain. Can fetch latest ranking and price in real time. .Pancake_NFT/nft-web3.py: Main file to fetch transaction and log through web3. .Pancake_NFT/pancake_nft.py: Original version to fetch log from bscscan. Now aborted. 0x00 Requirement Available for Python3.8+, recommend to use virtualenv to run this project. python-telegram-bot==13.7 web3==5.24.0 0x01 Usage Modify configuration in config.py TEST_BOT_TOKEN : tgbot token of test environment PROD_BOT_TOKEN: tgbot token of produce environment COLLECTION_CONTRACT_ADDRESS: […]

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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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