Deep Learning for Computer Vision with Python: ImageNet Bundle

Description

"ImageNet Bundle:The complete deep learning for computer vision experience. In this bundle, I demonstrate how to train large-scale neural networks on the massive ImageNet dataset. You just can't beat this bundle if you want to master deep learning for computer vision." [trouvé sur la page de l'éditeur]. 1 Introduction 2 Introduction 3 Training Networks Using Multiple GPUs 3.1 How Many GPUs Do I Need? 3.2 Performance Gains Using Multiple GPUs 3.3 Summary 4 What Is ImageNet? 4.1 The ImageNet Dataset 4.1.1 ILSVRC 4.2 Obtaining ImageNet 4.2.1 Requesting Access to the ILSVRC Challenge 4.2.2 Downloading Images Programmatically 4.2.3 Using External Services 4.2.4 ImageNet Development Kit 4.2.5 ImageNet Copyright Concerns 4.3 Summary 5 Preparing the ImageNet Dataset 5.1 Understanding the ImageNet File Structure 5.1.1 ImageNet “test” Directory 5.1.2 ImageNet “train” Directory 5.1.3 ImageNet “val” Directory 5.1.4 ImageNet “ImageSets” Directory 5.1.5 ImageNet “DevKit” Directory 5.2 Building the ImageNet Dataset 5.2.1 Your First ImageNet Configuration File 5.2.2 Our ImageNet Helper Utility 5.2.3 Creating List and Mean Files 5.2.4 Building the Compact Record Files 5.3 Summary 6 Training AlexNet on ImageNet 6.1 Implementing AlexNet 6.2 Training AlexNet 6.2.1 What About Training Plots? 6.2.2 Implementing the Training Script 6.3 Evaluating AlexNet 6.4 AlexNet Experiments 6.4.1 AlexNet: Experiment #1 6.4.2 AlexNet: Experiment #2 6.4.3 AlexNet: Experiment #3 6.5 Summary 7 Training VGGNet on ImageNet 7.1 Implementing VGGNet 7.2 Training VGGNet 7.3 Evaluating VGGNet 7.4 VGGNet Experiments 7.5 Summary 8 Training GoogLeNet on ImageNet 8.1 Understanding GoogLeNet 8.1.1 The Inception Module 8.1.2 GoogLeNet Architecture 8.1.3 Implementing GoogLeNet 8.1.4 Training GoogLeNet 8.2 Evaluating GoogLeNet 8.3 GoogLeNet Experiments 8.3.1 GoogLeNet: Experiment #1 8.3.2 GoogLeNet: Experiment #2 8.3.3 GoogLeNet: Experiment #3 8.4 Summary 9 Training ResNet on ImageNet 9.1 Understanding ResNet 9.2 Implementing ResNet 9.3 Training ResNet 9.4 Evaluating ResNet 9.5 ResNet Experiments 9.5.1 ResNet: Experiment #1 9.5.2 ResNet: Experiment #2 9.5.3 ResNet: Experiment #3 9.6 Summary 10 Training SqueezeNet on ImageNet 10.1 Understanding SqueezeNet 10.1.1 The Fire Module 10.1.2 SqueezeNet Architecture 10.1.3 Implementing SqueezeNet 10.2 Training SqueezeNet 10.3 Evaluating SqueezeNet 10.4 SqueezeNet Experiments 10.4.1 SqueezeNet: Experiment #1 10.4.2 SqueezeNet: Experiment #2 10.4.3 SqueezeNet: Experiment #3 10.4.4 SqueezeNet: Experiment #4 10.5 Summary 11 Case Study: Emotion Recognition 11.1 The Kaggle Facial Expression Recognition Challenge 11.1.1 The FER13 Dataset 11.1.2 Building the FER13 Dataset 11.2 Implementing a VGG-like Network 11.3 Training Our Facial Expression Recognizer 11.3.1 EmotionVGGNet: Experiment #1 11.3.2 EmotionVGGNet: Experiment #2 11.3.3 EmotionVGGNet: Experiment #3 11.3.4 EmotionVGGNet: Experiment #4 11.4 Evaluating our Facial Expression Recognizer 11.5 Emotion Detection in Real-time 11.6 Summary 12 Case Study: Correcting Image Orientation 12.1 The Indoor CVPR Dataset 12.1.1 Building the Dataset 12.2 Extracting Features 12.3 Training an Orientation Correction Classifier 12.4 Correcting Orientation 12.5 Summary 13 Case Study: Vehicle Identification 13.1 The Stanford Cars Dataset 13.1.1 Building the Stanford Cars Dataset 13.2 Fine-tuning VGG on the Stanford Cars Dataset 13.2.1 VGG Fine-tuning: Experiment #1 13.2.2 VGG Fine-tuning: Experiment #2 13.2.3 VGG Fine-tuning: Experiment #3 13.3 Evaluating our Vehicle Classifier 13.4 Visualizing Vehicle Classification Results 13.5 Summary 14 Case Study: Age and Gender Prediction 14.1 The Ethics of Gender Identification in Machine Learning 14.2 The Adience Dataset 14.2.1 Building the Adience Dataset 14.3 Implementing Our Network Architecture 14.4 Measuring “One-off” Accuracy 14.5 Training Our Age and Gender Predictor 14.6 Evaluating Age and Gender Prediction 14.7 Age and Gender Prediction Results 14.7.1 Age Results 14.7.2 Gender Results 14.8 Visualizing Results 14.8.1 Visualizing Results from Inside Adience 14.8.2 Understanding Face Alignment 14.8.3 Applying Age and Gender Prediction to Your Own Images 14.9 Summary 15 Faster R-CNNs 15.1 Object Detection and Deep Learning 15.1.1 Measuring Object Detector Performance 15.2 The (Faster) R-CNN Architecture 15.2.1 A Brief History of R-CNN 15.2.2 The Base Network 15.2.3 Anchors 15.2.4 Region Proposal Network (RPN) 15.2.5 Region of Interest (ROI) Pooling 15.2.6 Region-based Convolutional Neural Network 15.2.7 The Complete Training Pipeline 15.3 Summary 16 Training a Faster R-CNN From Scratch 16.1 The LISA Traffic Signs Dataset 16.2 Installing the TensorFlow Object Detection API 16.3 Training Your Faster R-CNN 16.3.1 Project Directory Structure 16.3.2 Configuration 16.3.3 A TensorFlow Annotation Class 16.3.4 Building the LISA + TensorFlow Dataset 16.3.5 A Critical Pre-Training Step 16.3.6 Configuring the Faster R-CNN 16.3.7 Training the Faster R-CNN 16.3.8 Suggestions When Working with the TFOD API 16.3.9 Exporting the Frozen Model Graph 16.3.10 Faster R-CNN on Images and Videos 16.4 Summary 17 Single Shot Detectors (SSDs) 17.1 Understanding Single Shot Detectors (SSDs) 17.1.1 Motivation 17.1.2 Architecture 17.1.3 MultiBox, Priors, and Fixed Priors 17.1.4 Training Methods 17.2 Summary 18 Training a SSD From Scratch 18.1 The Vehicle Dataset 18.2 Training Your SSD 18.2.1 Directory Structure and Configuration 18.2.2 Building the Vehicle Dataset 18.2.3 Training the SSD 18.2.4 SSD Results 18.2.5 Potential Problems and Limitations 18.3 Summary 19 Conclusions 19.1 Where to Now?