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Deep Learning Data Preprocessing

Manage and preprocess data for deep learning

Preprocessing data is a common first step in the deep learning workflow to prepare raw data in a format that the network can accept. 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.


Image LabelerLabel images for computer vision applications
Video LabelerLabel video for computer vision applications
Ground Truth LabelerLabel ground truth data for automated driving applications
Lidar LabelerLabel ground truth data in lidar point clouds
Signal LabelerLabel signal attributes, regions, and points of interest
Audio LabelerDefine and visualize ground-truth labels


Preprocess Deep Learning Data

Data Sets for Deep Learning

Discover data sets for various deep learning tasks.

Create and Explore Datastore for Image Classification

This example shows how to create, read, and augment an image datastore for use in training a deep learning network.

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.

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 Audio Labeler.

Label Pixels for Semantic Segmentation (Computer Vision Toolbox)

Label pixels for training a semantic segmentation network by using a labeling app.

Get Started with the Ground Truth Labeler (Automated Driving Toolbox)

Interactively label multiple lidar and video signals simultaneously.

Custom Labeling Functions (Signal Processing Toolbox)

Create and manage custom labeling functions.

Label Audio Using Audio Labeler (Audio Toolbox)

Interactively define and visualize ground-truth labels for audio datasets.

Customize Datastores

Datastores for Deep Learning

Learn how to use datastores in deep learning applications.

Prepare Datastore for Image-to-Image Regression

This example shows how to prepare a datastore for training an image-to-image regression network using the transform and combine functions of ImageDatastore.

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.

Classify Out-of-Memory Text Data Using Deep Learning

This example shows how to classify out-of-memory text data with a deep learning network using a transformed datastore.