About PyTorch Vision

PyTorch Vision is a library designed for computer vision research and applications. It provides pre-trained models, data loaders, and various image processing tools. This section will give you an overview of the key features and how to get started with PyTorch Vision.

Features

Pre-trained Models: PyTorch Vision offers a variety of pre-trained models such as ResNet, VGG, and MobileNet. These models can be used directly for inference or fine-tuned for specific tasks.
Data Loaders: Efficient data loading and augmentation tools to help with your training and evaluation data.
Transforms: A set of image transforms that can be applied to your data to augment it or prepare it for a specific model.

Getting Started

To install PyTorch Vision, you can use the following command:

pip install torchvision

Once installed, you can import and use the library as follows:

import torchvision

Example: Loading an Image

Here is an example of how to load and display an image using PyTorch Vision:

import torchvision.transforms as transforms
from torchvision.io import read_image
import matplotlib.pyplot as plt

# Define a transform to convert the image to a PyTorch tensor
transform = transforms.Compose([
    transforms.ToTensor(),
])

# Load an image
image = read_image("path/to/your/image.jpg")

# Apply the transform
image = transform(image)

# Display the image
plt.imshow(image.permute(1, 2, 0))
plt.show()

About PyTorch Vision

Features

Getting Started

Example: Loading an Image

Further Reading