PyTorchVideo: A Deep Learning Library for Video Understanding

• URL: http://arxiv.org/abs/2111.09887v1
• Date: Thu, 18 Nov 2021 18:59:58 GMT
• Title: PyTorchVideo: A Deep Learning Library for Video Understanding
• Authors: Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala, Yanghao Li, Yilei Li, Bo Xiong, Nikhila Ravi, Meng Li, Haichuan Yang, Jitendra Malik, Ross Girshick, Matt Feiszli, Aaron Adcock, Wan-Yen Lo, Christoph Feichtenhofer
• Abstract summary: PyTorchVideo is an open-source deep-learning library for video understanding tasks. It covers a full stack of video understanding tools including multimodal data loading, transformations, and models. The library is based on PyTorch and can be used by any training framework.
• Score: 71.89124881732015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/

Related papers

• Comgra: A Tool for Analyzing and Debugging Neural Networks [35.89730807984949]
  We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI. It can show both summary statistics and individual data points, compare early and late stages of training, focus on individual samples of interest, and visualize the flow of the gradient through the network.
  arXiv  Detail & Related papers  (2024-07-31T14:57:23Z)
• pyvene: A Library for Understanding and Improving PyTorch Models via Interventions [79.72930339711478]
  $textbfpyvene$ is an open-source library that supports customizable interventions on a range of different PyTorch modules. We show how $textbfpyvene$ provides a unified framework for performing interventions on neural models and sharing the intervened upon models with others.
  arXiv  Detail & Related papers  (2024-03-12T16:46:54Z)
• Spatio-temporal Prompting Network for Robust Video Feature Extraction [74.54597668310707]
  Frametemporal is one of the main challenges in the field of video understanding. Recent approaches exploit transformer-based integration modules to obtain quality-of-temporal information. We present a neat and unified framework called N-Temporal Prompting Network (NNSTP) It can efficiently extract video features by adjusting the input features in the network backbone.
  arXiv  Detail & Related papers  (2024-02-04T17:52:04Z)
• TorchBench: Benchmarking PyTorch with High API Surface Coverage [9.68698340637426]
  We propose TorchBench, a novel benchmark suite to study the performance of PyTorch software stack. TorchBench is able to comprehensively characterize the performance of the PyTorch software stack. We show two practical use cases of TorchBench.
  arXiv  Detail & Related papers  (2023-04-27T14:37:05Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• Small-Text: Active Learning for Text Classification in Python [23.87081733039124]
  small-text is an easy-to-use active learning library for Python. It offers pool-based active learning for single- and multi-label text classification.
  arXiv  Detail & Related papers  (2021-07-21T19:23:56Z)
• TorchKGE: Knowledge Graph Embedding in Python and PyTorch [0.0]
  TorchKGE is a Python module for knowledge graph (KG) embedding relying solely on PyTorch. It features a KG data structure, simple model interfaces and modules for negative sampling and model evaluation.
  arXiv  Detail & Related papers  (2020-09-07T09:21:34Z)
• mvlearn: Multiview Machine Learning in Python [103.55817158943866]
  mvlearn is a Python library which implements the leading multiview machine learning methods. The package can be installed from Python Package Index (PyPI) and the conda package manager.
  arXiv  Detail & Related papers  (2020-05-25T02:35:35Z)
• TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning [68.8204255655161]
  We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. It includes a command-line interface which allows users to apply transforms to image files without using Python.
  arXiv  Detail & Related papers  (2020-03-09T13:36:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.