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深度学习数据预处理

管理和预处理深度学习数据

预处理数据通常作为深度学习工作流中的第一步,旨在将原始数据准备为网络可以接受的格式。例如,您可以调整图像输入的大小以匹配图像输入层的大小。您还可以对数据进行预处理,以增强所需的特征或减少可能导致网络偏差的伪影。例如,您可以归一化或删除输入数据中的噪声。

您可以使用 MATLAB® 和 Deep Learning Toolbox™ 中提供的数据存储和函数通过调整大小等操作来预处理图像输入。其他 MATLAB 工具箱提供了用于标注、处理和增强深度学习数据的函数、数据存储和 App。您可以使用其他 MATLAB 工具箱中的专用工具,针对图像处理、目标检测、语义分割、信号处理、音频处理和文本分析等领域处理数据。

App

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

主题

预处理深度学习数据

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.

标注真实值训练数据

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.

自定义数据存储

Datastores for Deep Learning

Learn how to use datastores in deep learning applications.

为图像到图像的回归准备数据存储

此示例说明如何准备数据存储,以便使用 ImageDatastoretransformcombine 函数来训练图像到图像的回归网络。

使用无法放入内存的序列数据训练网络

此示例说明如何通过转换和合并数据存储基于无法放入内存的序列数据来训练深度学习网络。

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.