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Table of Contents
Preface; Books Website; Contents; Acronyms; List of Figures; 1 Traffic Sign Detection and Recognition; 1.1 Introduction; 1.2 Challenges; 1.3 Previous Work; 1.3.1 Template Matching; 1.3.2 Hand-Crafted Features; 1.3.3 Feature Learning; 1.3.4 ConvNets; 1.4 Summary; 2 Pattern Classification; 2.1 Formulation; 2.1.1 K-Nearest Neighbor; 2.2 Linear Classifier; 2.2.1 Training a Linear Classifier; 2.2.2 Hinge Loss; 2.2.3 Logistic Regression; 2.2.4 Comparing Loss Function; 2.3 Multiclass Classification; 2.3.1 One Versus One; 2.3.2 One Versus Rest; 2.3.3 Multiclass Hinge Loss
3.5.2 Software Libraries3.5.3 Evaluating a ConvNet; 3.6 Training a ConvNet; 3.6.1 Loss Function; 3.6.2 Initialization; 3.6.3 Regularization; 3.6.4 Learning Rate Annealing; 3.7 Analyzing Quantitative Results; 3.8 Other Types of Layers; 3.8.1 Local Response Normalization; 3.8.2 Spatial Pyramid Pooling; 3.8.3 Mixed Pooling; 3.8.4 Batch Normalization; 3.9 Summary; 3.10 Exercises; 4 Caffe Library; 4.1 Introduction; 4.2 Installing Caffe; 4.3 Designing Using Text Files; 4.3.1 Providing Data; 4.3.2 Convolution Layers; 4.3.3 Initializing Parameters; 4.3.4 Activation Layer; 4.3.5 Pooling Layer
4.3.6 Fully Connected Layer4.3.7 Dropout Layer; 4.3.8 Classification and Loss Layers; 4.4 Training a Network; 4.5 Designing in Python; 4.6 Drawing Architecture of Network; 4.7 Training Using Python; 4.8 Evaluating Using Python; 4.9 Save and Restore Networks; 4.10 Python Layer in Caffe; 4.11 Summary; 4.12 Exercises; 5 Classification of Traffic Signs; 5.1 Introduction; 5.2 Related Work; 5.2.1 Template Matching; 5.2.2 Hand-Crafted Features; 5.2.3 Sparse Coding; 5.2.4 Discussion; 5.2.5 ConvNets; 5.3 Preparing Dataset; 5.3.1 Splitting Data; 5.3.2 Augmenting Dataset
5.3.3 Static Versus One-the-Fly Augmenting5.3.4 Imbalanced Dataset; 5.3.5 Preparing the GTSRB Dataset; 5.4 Analyzing Training/Validation Curves; 5.5 ConvNets for Classification of Traffic Signs; 5.6 Ensemble of ConvNets; 5.6.1 Combining Models; 5.6.2 Training Different Models; 5.6.3 Creating Ensemble; 5.7 Evaluating Networks; 5.7.1 Misclassified Images; 5.7.2 Cross-Dataset Analysis and Transfer Learning; 5.7.3 Stability of ConvNet; 5.7.4 Analyzing by Visualization; 5.8 Analyzing by Visualizing; 5.8.1 Visualizing Sensitivity; 5.8.2 Visualizing the Minimum Perception
3.5.2 Software Libraries3.5.3 Evaluating a ConvNet; 3.6 Training a ConvNet; 3.6.1 Loss Function; 3.6.2 Initialization; 3.6.3 Regularization; 3.6.4 Learning Rate Annealing; 3.7 Analyzing Quantitative Results; 3.8 Other Types of Layers; 3.8.1 Local Response Normalization; 3.8.2 Spatial Pyramid Pooling; 3.8.3 Mixed Pooling; 3.8.4 Batch Normalization; 3.9 Summary; 3.10 Exercises; 4 Caffe Library; 4.1 Introduction; 4.2 Installing Caffe; 4.3 Designing Using Text Files; 4.3.1 Providing Data; 4.3.2 Convolution Layers; 4.3.3 Initializing Parameters; 4.3.4 Activation Layer; 4.3.5 Pooling Layer
4.3.6 Fully Connected Layer4.3.7 Dropout Layer; 4.3.8 Classification and Loss Layers; 4.4 Training a Network; 4.5 Designing in Python; 4.6 Drawing Architecture of Network; 4.7 Training Using Python; 4.8 Evaluating Using Python; 4.9 Save and Restore Networks; 4.10 Python Layer in Caffe; 4.11 Summary; 4.12 Exercises; 5 Classification of Traffic Signs; 5.1 Introduction; 5.2 Related Work; 5.2.1 Template Matching; 5.2.2 Hand-Crafted Features; 5.2.3 Sparse Coding; 5.2.4 Discussion; 5.2.5 ConvNets; 5.3 Preparing Dataset; 5.3.1 Splitting Data; 5.3.2 Augmenting Dataset
5.3.3 Static Versus One-the-Fly Augmenting5.3.4 Imbalanced Dataset; 5.3.5 Preparing the GTSRB Dataset; 5.4 Analyzing Training/Validation Curves; 5.5 ConvNets for Classification of Traffic Signs; 5.6 Ensemble of ConvNets; 5.6.1 Combining Models; 5.6.2 Training Different Models; 5.6.3 Creating Ensemble; 5.7 Evaluating Networks; 5.7.1 Misclassified Images; 5.7.2 Cross-Dataset Analysis and Transfer Learning; 5.7.3 Stability of ConvNet; 5.7.4 Analyzing by Visualization; 5.8 Analyzing by Visualizing; 5.8.1 Visualizing Sensitivity; 5.8.2 Visualizing the Minimum Perception