One important thing of working with PyTorch is specifying the device on which tensors and models should be located, such as CPU or GPU. Setting the default device globally in...

Knowing the device (e.g. CPU, GPU) on which a PyTorch model is located is useful for several reasons, such as hardware resource management, performance optimization, compatibility and portability, resource allocation...

When working with PyTorch, knowing the number of parameters in the model is important for various reasons, such as model optimization, memory management, and performance evaluation. This knowledge helps to...

Torchvision is a popular computer vision library in PyTorch that offers various pre-trained models. These models can be a convenient starting point for building computer vision applications. By understanding the...

Pillow (PIL) library provides various functions to manipulate, analyze, and display image data. When working with images, it's often useful to convert PyTorch tensors to PIL images for visualization and...

PyTorch uses tensors as its fundamental data structure. However, when working with images in Python, we often process them using Pillow (PIL) library. Therefore, we need to convert PIL image...

While PyTorch provides tools for building and training deep learning models, it does not provide many image processing functions that are available in OpenCV. Converting PyTorch tensor to OpenCV image...

In many times, it is necessary to process images using both OpenCV and PyTorch. To achieve this, we need to convert OpenCV image to PyTorch tensor, which is the fundamental...

There may be cases where you need to convert an OpenCV image to a Scikit image to take advantage of the image processing functions provided by scikit-image library. Thankfully, the...

In some cases, it may be necessary to convert Scikit image to an OpenCV image because OpenCV provides a wider range of image processing functions and computer vision algorithms compared...