Master Computer Vision with Tensorflow: A Hands-On Guide

Computer vision has revolutionized the way we interact with technology and has wide-ranging applications in various industries. TensorFlow, developed by Google, is a powerful framework that enables developers to build and deploy computer vision models with ease. In this article, we will explore the exciting world of computer vision using TensorFlow and learn how to apply it to real-life scenarios.

to Computer Vision

Computer vision is an interdisciplinary field that focuses on enabling machines to interpret, understand, and analyze visual information from images, videos, and other visual data sources. It encompasses techniques such as image recognition, object detection, image segmentation, and image generation.

With the advent of deep learning and advancements in neural networks, computer vision has achieved remarkable milestones in recent years. TensorFlow, as an open-source library, has played a pivotal role in driving these advancements forward.

Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

by Benjamin Planche(1st Edition, Kindle Edition)

4.1 out of 5

Language	:	English
File size	:	19742 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	374 pages
Screen Reader	:	Supported
Paperback	:	130 pages
Item Weight	:	10.2 ounces
Dimensions	:	7 x 0.3 x 10 inches

FREE

The Power of TensorFlow in Computer Vision

TensorFlow provides a comprehensive set of tools, libraries, and resources for computer vision tasks. Its flexibility and scalability make it a popular choice among machine learning enthusiasts and professionals alike.

There are several key features of TensorFlow that make it particularly suitable for computer vision:

1. Efficient computation: TensorFlow's computational graph allows for efficient matrix computations, which are essential for handling large-scale image datasets.
2. Deep neural network support: TensorFlow provides a wide range of pre-built models and tools for training and deploying deep neural networks for various computer vision tasks.
3. Easy model deployment: TensorFlow's extensive ecosystem enables seamless deployment of computer vision models to different platforms, including mobile devices and cloud environments.

Getting Started with Computer Vision in TensorFlow

Now that we understand the power of TensorFlow in computer vision, let's dive into the practical aspects of getting started with hands-on projects.

Installing TensorFlow

The first step is to install TensorFlow on your machine. TensorFlow supports multiple installation options, such as pip, Docker, and Anaconda. Choose the installation method that suits your requirements and follow the official documentation to set it up.

Exploring Computer Vision Tasks

TensorFlow offers numerous pre-built models and datasets for various computer vision tasks. Some common tasks include:

• Image classification: Assigning labels to images from a predefined set of categories.
• Object detection: Identifying and localizing objects within an image.
• Image segmentation: Partitioning an image into different regions based on semantic understanding.
• Generative models: Creating new images based on training data.

Choose a task that interests you and explore the available TensorFlow resources for that specific task.

Building and Training a Computer Vision Model

Once you have chosen a task and collected the required datasets, it's time to build and train your computer vision model using TensorFlow.

TensorFlow provides a high-level API called Keras, which makes it easy to build and train deep neural networks. You can start with a pre-trained model and fine-tune it on your specific dataset, or build your own model from scratch using various layers and activation functions.

It's crucial to tune the hyperparameters of your model to achieve optimal performance. Experiment with different architectures, learning rates, and batch sizes to find the best configuration for your task.

Evaluating and Deploying the Model

Once your model is trained, it's important to evaluate its performance on a separate validation dataset. Evaluate metrics such as accuracy, precision, recall, and F1 score to gain insights into its effectiveness.

After validating the model, you can deploy it for inference on new, unseen data. TensorFlow provides various options for model deployment, including the TensorFlow Serving library for high-performance serving, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for browser-based applications.

Real-World Applications of Computer Vision with TensorFlow

Computer vision has numerous real-world applications across various industries:

• Autonomous vehicles: Computer vision plays a key role in enabling self-driving cars to identify and understand their surroundings.
• Healthcare: Medical imaging relies heavily on computer vision for tasks such as disease detection and analysis.
• Retail: Computer vision can enhance customer experiences, automate inventory management, and identify customer behavior patterns.
• Security and surveillance: Video analysis using computer vision can improve security monitoring systems by detecting anomalies and identifying potential threats.

These are just a few examples, and the applications of computer vision in practical scenarios are expanding rapidly.

Computer vision has made remarkable strides in recent years, thanks to the power of frameworks like TensorFlow. With TensorFlow, developers can build sophisticated computer vision models and deploy them across various platforms with ease.

By diving into the world of computer vision and experiencing hands-on projects, you can unlock a multitude of opportunities to apply this cutting-edge technology in your own domain. Whether it's autonomous vehicles, healthcare, retail, or security, computer vision has the potential to revolutionize industries and improve countless aspects of our lives.

Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

by Benjamin Planche(1st Edition, Kindle Edition)

4.1 out of 5

Language	:	English
File size	:	19742 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	374 pages
Screen Reader	:	Supported
Paperback	:	130 pages
Item Weight	:	10.2 ounces
Dimensions	:	7 x 0.3 x 10 inches

FREE

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more

Key Features

• Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
• Apply modern solutions to a wide range of applications such as object detection and video analysis
• Learn how to run your models on mobile devices and web pages and improve their performance

Book Description

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.

Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO),Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.

By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.

What you will learn

• Create your own neural networks from scratch
• Classify images with modern architectures including Inception and ResNet
• Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
• Tackle problems faced when developing self-driving cars and facial emotion recognition systems
• Boost your application's performance with transfer learning, GANs, and domain adaptation
• Use recurrent neural networks (RNNs) for video analysis
• Optimize and deploy your networks on mobile devices and in the browser

Who this book is for

If you're new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you're an expert curious about the new TensorFlow 2 features, you'll find this book useful.

While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

Table of Contents

1. Computer Vision and Neural Networks
2. TensorFlow Basics and Training a Model
3. Modern Neural networks
4. Influential Classification Tools
5. Object Detection Models
6. Enhancing and Segmenting Images
7. Training on Complex and Scarce Datasets
8. Video and Recurrent Neural Networks
9. Optimizing Models and Deploying on Mobile Devices
10. Appendix