Price: $19.95
(as of Dec 27,2024 03:33:22 UTC – Details)

ASIN ‏ : ‎ B0B6ZBHS91
Publisher ‏ : ‎ Apress (July 18, 2022)
Publication date ‏ : ‎ July 18, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 21640 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 380 pages

Computer Vision Projects with PyTorch: Design and Develop Production-Grade Models

In recent years, computer vision has emerged as a powerful tool in various industries, from healthcare to autonomous driving. PyTorch, a popular open-source machine learning library, has become the go-to framework for developing computer vision models due to its flexibility, ease of use, and strong community support.

In this post, we will explore how to design and develop production-grade computer vision models using PyTorch. We will cover key concepts, best practices, and practical tips for building robust and scalable models that can be deployed in real-world applications.

Key topics we will discuss include:

1. Data preprocessing: The first step in any computer vision project is to preprocess the data. We will show you how to load, transform, and augment image data using PyTorch’s powerful data handling capabilities.

2. Model design: We will walk you through the process of designing a convolutional neural network (CNN) architecture for your computer vision task. We will cover popular architectures such as ResNet, VGG, and MobileNet, as well as techniques for improving model performance, such as transfer learning and fine-tuning.

3. Training and evaluation: We will show you how to train your model using PyTorch’s training loop and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score. We will also discuss techniques for optimizing hyperparameters and preventing overfitting.

4. Deployment: Once you have trained and evaluated your model, the next step is to deploy it in a real-world application. We will discuss different deployment strategies, including serving your model using a REST API, integrating it into a web application, or deploying it to the cloud using services like AWS or Azure.

By the end of this post, you will have a solid understanding of how to design and develop production-grade computer vision models using PyTorch. Whether you are a beginner looking to get started with computer vision or an experienced practitioner looking to improve your skills, this post will provide you with the knowledge and tools you need to succeed in this exciting field. Let’s get started!
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