Deep Learning Data Preprocessing
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 Labeler||Label images for computer vision applications|
|Video Labeler||Label video for computer vision applications|
|Ground Truth Labeler||Label ground truth data for automated driving applications|
|Lidar Labeler||Label ground truth data in lidar point clouds|
|Signal Labeler||Label signal attributes, regions, and points of interest, and extract features|
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, or Signal 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 Spoken Words in Audio Signals (Signal Processing Toolbox)
Use Signal Labeler to label spoken words in an audio signal.
- 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
- 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.