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  • PyTorch
    PyTorch Foundation is the deep learning community home for the open source PyTorch framework and ecosystem
  • Get Started - PyTorch
    For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core
  • Previous PyTorch Versions
    Access and install previous PyTorch versions, including binaries and instructions for all platforms
  • Transforming images, videos, boxes and more - PyTorch
    Transforming images, videos, boxes and more Torchvision supports common computer vision transformations in the torchvision transforms v2 module Transforms can be used to transform and augment data, for both training or inference The following objects are supported: Images as pure tensors, Image or PIL image Videos as Video Axis-aligned and rotated bounding boxes as BoundingBoxes Segmentation
  • PyTorch 2. x
    Introducing PyTorch 2 0, our first steps toward the next generation 2-series release of PyTorch Over the last few years we have innovated and iterated from PyTorch 1 0 to the most recent 1 13 and moved to the newly formed PyTorch Foundation, part of the Linux Foundation
  • PyTorch documentation — PyTorch 2. 9 documentation
    PyTorch documentation # PyTorch is an optimized tensor library for deep learning using GPUs and CPUs Features described in this documentation are classified by release status: Stable (API-Stable): These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation
  • Models and pre-trained weights - PyTorch
    TorchVision offers pre-trained weights for every provided architecture, using the PyTorch torch hub Instancing a pre-trained model will download its weights to a cache directory
  • PyTorch Examples — PyTorchExamples 1. 11 documentation
    PyTorch Examples This pages lists various PyTorch examples that you can use to learn and experiment with PyTorch




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