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Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision
Technical requirements
Working with the basic building blocks of the Keras API
Getting ready
How to do it
How it works
See also
Loading images using the Keras API
Getting ready
How to do it
How it works
See also
Loading images using the tf.data.Dataset API
How to do it
How it works
See also
Saving and loading a model
How to do it
How it works
There's more
Visualizing a model's architecture
Getting ready
How to do it
How it works
Creating a basic image classifier
Getting ready
How to do it
How it works
See also
Chapter 2: Performing Image Classification
Technical requirements
Creating a binary classifier to detect smiles
Getting ready
How to do it
How it works
See also
Creating a multi-class classifier to play rock paper scissors
Getting ready
How to do it
How it works
Creating a multi-label classifier to label watches
Getting ready
How to do it
How it works
See also
Implementing ResNet from scratch
Getting ready
How to do it
How it works
See also
Classifying images with a pre-trained network using the Keras API
Getting ready
How to do it
How it works
See also
Classifying images with a pre-trained network using TensorFlow Hub
Getting ready
How to do it
How it works
See also
Using data augmentation to improve performance with the Keras API
Getting ready
How to do it
How it works
See also
Using data augmentation to improve performance with the tf.data and tf.image APIs
Getting ready
How to do it
How it works
See also
Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning
Technical requirements
Implementing a feature extractor using a pre-trained network
Getting ready
How to do it
How it works
See also
Training a simple classifier on extracted features
Getting ready
How to do it
How it works
See also
Spot-checking extractors and classifiers
Getting ready
How to do it
How it works
Using incremental learning to train a classifier
Getting ready
How to do it
How it works
Fine-tuning a network using the Keras API
Getting ready
How to do it
How it works
See also
Fine-tuning a network using TFHub
Getting ready
How to do it
How it works
See also
Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution
Technical requirements
Implementing DeepDream
Getting ready
How to do it
How it works
See also
Generating your own dreamy images
Getting ready
How to do it
How it works
Implementing Neural Style Transfer
Getting ready
How to do it
How it works
See also
Applying style transfer to custom images
Getting ready
How to do it
How it works
See also
Applying style transfer with TFHub
Getting ready
How to do it
How it works
See also
Improving image resolution with deep learning
Getting ready
How to do it
How it works
See also
Chapter 5: Reducing Noise with Autoencoders
Technical requirements
Creating a simple fully connected autoencoder
Getting ready
How to do it
How it works
See also
Creating a convolutional autoencoder
Getting ready
How to do it
How it works
See also
Denoising images with autoencoders
Getting ready
How to do it
How it works
Spotting outliers using autoencoders
Getting ready
How to do it
How it works
Creating an inverse image search index with deep learning
Getting ready
How to do it
How it works
See also
Implementing a variational autoencoder
Getting ready
How to do it
How it works
See also
Chapter 6: Generative Models and Adversarial Attacks
Technical requirements
Implementing a deep convolutional GAN
Getting ready
How to do it
How it works
See also
Using a DCGAN for semi-supervised learning
Getting ready
How to do it
How it works
See also
Translating images with Pix2Pix
Getting ready
How to do it
How it works
See also
Translating unpaired images with CycleGAN
Getting ready
How to do it
How it works
See also
Implementing an adversarial attack using the Fast Gradient Signed Method
Getting ready
How to do it
How it works
See also
Chapter 7: Captioning Images with CNNs and RNNs
Technical requirements
Implementing a reusable image caption feature extractor
Getting ready
How to do it
How it works
See also
Implementing an image captioning network
Getting ready
How to do it
How it works
See also
Generating captions for your own photos
Getting ready
How to do it
How it works
Implementing an image captioning network on COCO with attention
Getting ready
How to do it
Chapter 8: Fine-Grained Understanding of Images through Segmentation
Technical requirements
Creating a fully convolutional network for image segmentation
Getting ready
How to do it
How it works
See also
Implementing a U-Net from scratch
Getting ready
How to do it
How it works
See also
Implementing a U-Net with transfer learning
Getting ready
How to do it
How it works
See also
Segmenting images using Mask-RCNN and TensorFlow Hub
Getting ready
How to do it
How it works
See also
Chapter 9: Localizing Elements in Images with Object Detection
Technical requirements
Creating an object detector with image pyramids and sliding windows
Getting ready
How to do it
How it works
See also
Detecting objects with YOLOv3
Getting ready
How it works
See also
Training your own object detector with TensorFlow's Object Detection API
Getting ready
How to do it
How it works
See also
Detecting objects using TFHub
Getting ready
How to do it
How it works
See also
Chapter 10: Applying the Power of Deep Learning to Videos
Technical requirements
Detecting emotions in real time
Getting ready
How to do it
How it works
See also
Recognizing actions with TensorFlow Hub
Getting ready
How to do it
How it works
See also
Generating the middle frames of a video with TensorFlow Hub
Getting ready
How to do it
How it works
See also
Performing text-to-video retrieval with TensorFlow Hub
Getting ready
How to do it
How it works
See also
Chapter 11: Streamlining Network Implementation with AutoML
Technical requirements
Creating a simple image classifier with AutoKeras
How to do it
How it works
See also
Creating a simple image regressor with AutoKeras
Getting ready
How to do it
How it works
See also
Exporting and importing a model in AutoKeras
How to do it
How it works
See also
Controlling architecture generation with AutoKeras' AutoModel
How to do it
How it works
See also
Predicting age and gender with AutoKeras
Getting ready
How to do it
How it works
See also
Chapter 12: Boosting Performance
Technical requirements
Using convolutional neural network ensembles to improve accuracy
Getting ready
How to do it
How it works
See also
Using test time augmentation to improve accuracy
Getting ready
How to do it
How it works
Using rank-N accuracy to evaluate performance
Getting ready
How to do it
How it works
See also
Using label smoothing to increase performance
Getting ready
How to do it
How it works
Checkpointing model
How to do it
Customizing the training process using tf.GradientTape
How to do it
How it works
Getting ready
How to do it
How it works
See also
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