TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges

Martínez, Jesús

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商品描述

Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques

Key Features

  • Develop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.x
  • Discover practical recipes to overcome various challenges faced while building computer vision models
  • Enable machines to gain a human level understanding to recognize and analyze digital images and videos

Book Description

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

What you will learn

  • Understand how to detect objects using state-of-the-art models such as YOLOv3
  • Use AutoML to predict gender and age from images
  • Segment images using different approaches such as FCNs and generative models
  • Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentation
  • Enable machines to recognize people's emotions in videos and real-time streams
  • Access and reuse advanced TensorFlow Hub models to perform image classification and object detection
  • Generate captions for images using CNNs and RNNs

Who this book is for

This book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

  1. Getting Started with TensorFlow 2.x for Computer Vision
  2. Performing Image Classification
  3. Harnessing the Power of Pre-Trained Networks with Transfer Learning
  4. Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution
  5. Reducing Noise with Autoencoders
  6. Generative Models and Adversarial Attacks
  7. Captioning Images with CNNs and RNNs
  8. Fine-Grained Understanding of Images through Segmentation
  9. Localizing Elements in Images with Object Detection
  10. Applying the Power of Deep Learning to Videos
  11. Streamlining Network Implementation with AutoML
  12. Boosting Performance

商品描述(中文翻譯)

熟悉最先進的技術,使用機器學習和深度學習技術來定制訓練流程,提升計算機視覺模型的性能。

主要特點:

- 使用TensorFlow 2.x開發、訓練和使用計算機視覺任務的深度學習算法。
- 發現實用的解決方案,克服構建計算機視覺模型時遇到的各種挑戰。
- 使機器能夠達到人類水平的理解,識別和分析數字圖像和視頻。

書籍描述:

計算機視覺是一個科學領域,使機器能夠識別和處理數字圖像和視頻。本書專注於使用TensorFlow執行各種計算機視覺任務的獨立解決方案。

本書首先介紹了計算機視覺的深度學習基礎,並介紹了TensorFlow 2.x的關鍵功能,如Keras和tf.data.Dataset API。然後,您將學習常見的計算機視覺任務的內幕,例如圖像分類,遷移學習,圖像增強和風格化,以及物體檢測。本書還涵蓋了在反向圖像搜索索引和圖像去噪等領域中使用的自編碼器,並提供了有關配方中使用的各種架構的見解,例如卷積神經網絡(CNN),基於區域的CNN(R-CNN),VGGNet和You Only Look Once(YOLO)。

接下來,您將發現解決構建各種計算機視覺應用程序時遇到的任何問題的技巧和訣竅。最後,您將深入研究更高級的主題,如生成對抗網絡(GAN),視頻處理和AutoML,並以提高網絡性能的技術為重點。

通過閱讀本書,您將能夠自信地使用TensorFlow 2.x解決各種計算機視覺問題。

您將學到什麼:

- 瞭解如何使用YOLOv3等最先進的模型來檢測物體。
- 使用AutoML從圖像中預測性別和年齡。
- 使用不同方法(如FCN和生成模型)對圖像進行分割。
- 學習如何通過排名N準確性,標籤平滑和測試時間增強來提高網絡性能。
- 使機器能夠識別視頻和實時流中的人的情緒。
- 訪問並重複使用高級的TensorFlow Hub模型,執行圖像分類和物體檢測。
- 使用CNN和RNN為圖像生成標題。
- 細粒度理解圖像通過分割。
- 使用物體檢測在圖像中定位元素。
- 將深度學習的威力應用於視頻。
- 使用AutoML簡化網絡實現。
- 提高性能。

本書適合計算機視覺開發人員和工程師,以及尋找解決計算機視覺中常見問題的解決方案的深度學習從業人員。您將學習如何使用現代機器學習(ML)技術和深度學習架構執行各種計算機視覺任務。需要基本的Python編程和計算機視覺知識。

作者簡介

Jesús Martínez is the founder of the computer vision e-learning site DataSmarts. He is a computer vision expert and has worked on a wide range of projects in the field, such as a piece of people-counting software fed with images coming from an RGB camera and a depth sensor, using OpenCV and TensorFlow. He developed a self-driving car in a simulation, using a convolutional neural network created with TensorFlow, that worked solely with visual inputs. Also, he implemented a pipeline that uses several advanced computer vision techniques to track lane lines on the road, as well as providing extra information such as curvature degree.

作者簡介(中文翻譯)

Jesús Martínez 是電腦視覺線上學習網站 DataSmarts 的創辦人。他是一位電腦視覺專家,在這個領域中參與了多個專案,例如使用 OpenCV 和 TensorFlow 開發了一個從 RGB 相機和深度感測器獲取圖像的人數統計軟體。他還使用 TensorFlow 創建了一個卷積神經網路,在模擬中實現了一輛僅使用視覺輸入的自駕車。此外,他還實現了一個流程,使用多種先進的電腦視覺技術來追蹤道路上的車道線,並提供額外的信息,如曲率度數。

目錄大綱

  1. Getting Started with TensorFlow 2.x for Computer Vision
  2. Performing Image Classification
  3. Harnessing the Power of Pre-Trained Networks with Transfer Learning
  4. Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution
  5. Reducing Noise with Autoencoders
  6. Generative Models and Adversarial Attacks
  7. Captioning Images with CNNs and RNNs
  8. Fine-Grained Understanding of Images through Segmentation
  9. Localizing Elements in Images with Object Detection
  10. Applying the Power of Deep Learning to Videos
  11. Streamlining Network Implementation with AutoML
  12. Boosting Performance

目錄大綱(中文翻譯)

- 使用 TensorFlow 2.x 進行計算機視覺入門
- 執行圖像分類
- 利用預訓練網絡和轉移學習的威力
- 通過 DeepDream、神經風格轉換和圖像超解析度增強和美化圖像
- 使用自編碼器減少噪音
- 生成模型和對抗攻擊
- 使用 CNN 和 RNN 為圖像加上標題
- 通過分割對圖像進行細粒度理解
- 使用物體檢測在圖像中定位元素
- 將深度學習的威力應用於視頻
- 使用 AutoML 簡化網絡實現
- 提升性能