دانلود کتاب تسلط بر کامپیوتر ویژن با PyTorch 2.0 – راه حل های بینایی کامپیوتری با کارایی بالا را کشف، طراحی و بسازید

PyTorch, at its core, is a free machine-learning framework. It offers two high-level features: deep neural networks constructed using a tape-based autograd system and tensor calculations with excellent GPU acceleration support. To put it simply, PyTorch includes all the tools required to create and train deep learning models. However, PyTorch’s ‘Pythonic’ aspect sets it apart from other machine learning frameworks. PyTorch is not for binding Python into a rigid C++ framework. It is designed to be tightly linked with Python and uses Python’s strength to provide a fluid and adaptable user experience. Nearly the same procedures apply when using PyTorch as when using NumPy. And because PyTorch tensors and NumPy arrays are so similar, you can easily swap between the two. What makes PyTorch particularly attractive for computer vision is its extensive ecosystem of libraries and tools that specifically cater to vision tasks. TorchVision, a package in PyTorch, has datasets, models (including pre-trained models), and transformation functions for images. This means that PyTorch provides us with ready-to-use tools and functionalities that can help us in a wide array of vision tasks, thereby letting us focus on the unique aspects of our specific problems. But PyTorch is not just about simplicity and ease of use. It’s also about power and control. PyTorch uses a method called “define-by-run” for building computational graphs, in contrast to the “define-and-run” method used in many other frameworks. This means that the computational graph, a series of operations that defines a mathematical model, is defined on the go as the operations occur. This provides a lot of flexibility and lets us do complex things with our models. And it goes beyond that. PyTorch has improved in both power and usability with version 2.0. The history of PyTorch will be covered in more detail on the next pages, along with instructions on how to install and set up PyTorch and an examination of the main changes and additions made in PyTorch 2.0. We’re about to blast off into the realm of PyTorch 2.0 and computer vision, so buckle up! PyTorch has positioned itself as a go-to library for Computer Vision tasks. It offers a perfect blend of flexibility and power. The ease with which you can build, train, and tweak deep learning models makes PyTorch an attractive choice for researchers and developers in the field of computer vision. But the appeal doesn’t stop at flexibility. PyTorch also provides efficiency and performance allowing it to scale from prototyping stages to large-scale deployment. One of the primary reasons why PyTorch is widely used in computer vision is its rich ecosystem of libraries and tools explicitly designed for vision tasks. Torchvision, a package in PyTorch, includes an array of preprocessed datasets and pre-trained models, such as ResNet and VGG, which have established benchmarks in various computer vision tasks. It also provides transformation functions to perform image manipulations, a crucial aspect in any computer vision pipeline. With PyTorch’s “define-by-run” paradigm, the dynamic computation graph becomes an excellent fit for the sequence of operations typically found in computer vision tasks. This approach provides researchers and developers with maximum flexibility in designing and experimenting with complex architectures, which is often necessary in the rapidly evolving field of computer vision. Furthermore, the enhancements brought about by PyTorch 2.0 have strengthened its position even more. Improvements in performance, new APIs, and features like mobile deployment make PyTorch 2.0 a compelling choice for any computer vision task – from image classification and object detection to semantic segmentation and beyond. In summary, the combination of PyTorch’s intuitive design, coupled with its strong performance characteristics, has made it a favorite among the computer vision community. As we move forward in this book, you will get a hands-on experience of using PyTorch for various exciting and impactful computer vision tasks and use cases.