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.

• Augment Images for Deep Learning Workflows Using Image Processing Toolbox
• Augment Pixel Labels for Semantic Segmentation
• Augment Bounding Boxes for Object Detection
• Speech Command Recognition Using Deep Learning
• Classify Text Data Using Deep Learning

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, orAudio Labeler.

Label Pixels for Semantic Segmentation(计算机视觉工具箱)

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

这个例子展示了如何准备的数据存储training an image-to-image regression network using thetransformandcombinefunctions ofImageDatastore.

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.