Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟮𝟰 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝗵𝗮𝘀 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗣𝘆𝗧𝗼𝗿𝗰𝗵𝗩𝗶𝗱𝗲𝗼: 𝗔 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝗩𝗶𝗱𝗲𝗼 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗣𝘆𝗧𝗼𝗿𝗰𝗵𝗩𝗶𝗱𝗲𝗼: 𝗔 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝗩𝗶𝗱𝗲𝗼 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 🔸 This paper is published in arxiv2021. 🔸 PyTorchVideo is a deep learning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; Key features include: 🔸Based on PyTorch: Built using PyTorch. Makes it easy to use all of the PyTorch-ecosystem components. 🔸Reproducible Model Zoo: Variety of state of the art pretrained video models and their associated benchmarks that are ready to use. Complementing the model zoo, PyTorchVideo comes with extensive data loaders supporting different datasets. 🔸Efficient Video Components: Video-focused fast and efficient components that are easy to use. Supports accelerated inference on hardware. #computervision #artificialintelligence #innovation Meta