Preprocess Data for Deep Neural Networks - MATLAB & Simulink (original) (raw)

Manage and preprocess data for deep learning

Preprocessing data to ensure that it is in a format that the network can accept is a common first step in deep learning workflows. For example, you can resize image input to match the size of an image input layer. You can also preprocess data to enhance desired features or reduce artifacts that can bias the network. For example, you can normalize or remove noise from input data.

You can preprocess image input with operations such as resizing by using datastores and functions available in MATLAB® and Deep Learning Toolbox™. Other MATLAB toolboxes offer functions, datastores, and apps for labeling, processing, and augmenting deep learning data. Use specialized tools from other MATLAB toolboxes to process data for domains such as image processing, object detection, semantic segmentation, signal processing, audio processing, and text analytics.

Apps

Functions

Topics

Preprocess Deep Learning Data

• Data Sets for Deep Learning
  Discover data sets for various deep learning tasks.
• Preprocess Images for Deep Learning
  Learn how to resize images for training, prediction, and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.
• Preprocess Volumes for Deep Learning
  Read and preprocess volumetric image and label data for 3-D deep learning.
• Preprocess Data for Domain-Specific Deep Learning Applications
  Perform deterministic or randomized data processing for domains such as image processing, object detection, semantic segmentation, signal and audio processing, and text analytics.
  • Augment Images for Deep Learning Workflows
  • Augment Pixel Labels for Semantic Segmentation
  • Augment Bounding Boxes for Object Detection
  • Train Speech Command Recognition Model Using Deep Learning
  • Classify Text Data Using Deep Learning

Customize Datastores

• Datastores for Deep Learning
  Learn how to use datastores in deep learning applications.
• Train Network Using Out-of-Memory Sequence Data
  This example shows how to train a deep learning network on out-of-memory sequence data by transforming and combining datastores.
• Classify Text Data Using Convolutional Neural Network
  This example shows how to classify text data using a convolutional neural network.
• Optimize Datastores for Deep Learning Performance
  Explore methods for speeding up deep learning workflows that use datastores.
• Develop Custom Mini-Batch Datastore
  Create a fully customized mini-batch datastore that contains training and test data sets for network training, prediction, and classification.

Label Ground Truth Training Data

• Choose an App to Label Ground Truth Data
  Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, Signal Labeler, or Medical Image Labeler.
• Get Started with Ground Truth Labelling (Automated Driving Toolbox)
  Interactively label multiple lidar and video signals simultaneously.
• Custom Labeling Functions (Signal Processing Toolbox)
  Create and manage custom labeling functions.
• Label Spoken Words in Audio Signals (Signal Processing Toolbox)
  Use Signal Labeler to label spoken words in an audio signal.
• Label Pixels for Semantic Segmentation (Computer Vision Toolbox)
  Label pixels for training a semantic segmentation network by using a labeling app.

Featured Examples