Main Content

Data Sets for Deep Learning

Use these data sets to get started with deep learning applications.

Note

Some of the code used in these data set descriptions use functions attached to examples as supporting files. To use these functions, open the examples as live scripts.

Image Data Sets

Data SetDescriptionTask

Digits

The Digits data set consists of 10,000 synthetic grayscale images of handwritten digits. Each image is 28-by-28 pixels and has an associated label denoting which digit the image represents (0–9). Each image has been rotated by a certain angle. When loading the images as arrays, you can also load the rotation angle of the image.

Load the Digits data as in-memory numeric arrays using the digitTrain4DArrayData and digitTest4DArrayData functions.

[XTrain,YTrain,anglesTrain] = digitTrain4DArrayData;
[XTest,YTest,anglesTest] = digitTest4DArrayData;

For examples showing how to process this data for deep learning, see Monitor Deep Learning Training Progress and Train Convolutional Neural Network for Regression.

Image classification and image regression

Load the Digits data as an image datastore using the imageDatastore function and specify the folder containing the image data.

dataFolder = fullfile(toolboxdir('nnet'),'nndemos','nndatasets','DigitDataset');
imds = imageDatastore(dataFolder, ...
    'IncludeSubfolders',true,'LabelSource','foldernames');

For an example showing how to process this data for deep learning, see Create Simple Deep Learning Neural Network for Classification.

Image classification

Omniglot

The Omniglot data set contains character sets for 50 alphabets, divided into 30 sets for training and 20 sets for testing [1]. Each alphabet contains a number of characters, from 14 for Ojibwe (Canadian Aboriginal syllabics) to 55 for Tifinagh. Finally, each character has 20 handwritten observations.

Download and extract the Omniglot data set from https://github.com/brendenlake/omniglot. Set downloadFolder to the location of the data.

downloadFolder = tempdir;

url = "https://github.com/brendenlake/omniglot/raw/master/python";

urlTrain = url + "/images_background.zip";
urlTest = url + "/images_evaluation.zip";

filenameTrain = fullfile(downloadFolder,"images_background.zip");
filenameTest = fullfile(downloadFolder,"images_evaluation.zip");

dataFolderTrain = fullfile(downloadFolder,"images_background");
dataFolderTest = fullfile(downloadFolder,"images_evaluation");

if ~exist(dataFolderTrain,"dir")
    fprintf("Downloading Omniglot training data set (4.5 MB)... ")
    websave(filenameTrain,urlTrain);
    unzip(filenameTrain,downloadFolder);
    fprintf("Done.\n")
end

if ~exist(dataFolderTest,"dir")
    fprintf("Downloading Omniglot test data (3.2 MB)... ")
    websave(filenameTest,urlTest);
    unzip(filenameTest,downloadFolder);
    fprintf("Done.\n")
end

To load the training and test data as image datastores, use the imageDatastore function. Specify the labels manually by extracting the labels from the file names and setting the Labels property.

imdsTrain = imageDatastore(dataFolderTrain, ...
    'IncludeSubfolders',true, ...
    'LabelSource','none');

files = imdsTrain.Files;
parts = split(files,filesep);
labels = join(parts(:,(end-2):(end-1)),'_');
imdsTrain.Labels = categorical(labels);

imdsTest = imageDatastore(dataFolderTest, ...
    'IncludeSubfolders',true, ...
    'LabelSource','none');

files = imdsTest.Files;
parts = split(files,filesep);
labels = join(parts(:,(end-2):(end-1)),'_');
imdsTest.Labels = categorical(labels);

For an example showing how to process this data for deep learning, see Train a Twin Neural Network to Compare Images.

Image similarity

Flowers

Image credits: [3] [4] [5] [6]

The Flowers data set contains 3670 images of flowers belonging to five classes (daisy, dandelion, roses, sunflowers, and tulips) [2].

Download and extract the Flowers data set from http://download.tensorflow.org/example_images/flower_photos.tgz. The data set is about 218 MB. Set downloadFolder to the location of the data.

url = 'http://download.tensorflow.org/example_images/flower_photos.tgz';
downloadFolder = tempdir;
filename = fullfile(downloadFolder,'flower_dataset.tgz');

dataFolder = fullfile(downloadFolder,'flower_photos');
if ~exist(dataFolder,'dir')
    fprintf("Downloading Flowers data set (218 MB)... ")
    websave(filename,url);
    untar(filename,downloadFolder)
    fprintf("Done.\n")
end

Load the data as an image datastore using the imageDatastore function and specify the folder containing the image data.

imds = imageDatastore(dataFolder, ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');

For an example showing how to process this data for deep learning, see Train Generative Adversarial Network (GAN).

Image classification

Example Food Images

The Example Food Images data set contains 978 photographs of food in nine classes (caesar_salad, caprese_salad, french_fries, greek_salad, hamburger, hot_dog, pizza, sashimi, and sushi).

Download the Example Food Images data set using the downloadSupportFile function and extract the images using the unzip function. This data set is about 77 MB.

fprintf("Downloading Example Food Image data set (77 MB)... ")
filename = matlab.internal.examples.downloadSupportFile('nnet', ...
    'data/ExampleFoodImageDataset.zip');
fprintf("Done.\n")

filepath = fileparts(filename);
dataFolder = fullfile(filepath,'ExampleFoodImageDataset');
unzip(filename,dataFolder);

For an example showing how to process this data for deep learning, see View Network Behavior Using tsne.

Image classification

CIFAR-10

(Representative example)

The CIFAR-10 data set contains 60,000 color images of size 32-by-32 pixels, belonging to 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck) [7]. There are 6,000 images per class.

The data set is split into a training set with 50,000 images and a test set with 10,000 images. This data set is one of the most widely used data sets for testing new image classification models.

Download and extract the CIFAR-10 data set from https://www.cs.toronto.edu/%7Ekriz/cifar-10-matlab.tar.gz. The data set is about 175 MB. Set downloadFolder to the location of the data.

url = 'https://www.cs.toronto.edu/~kriz/cifar-10-matlab.tar.gz';
downloadFolder = tempdir;
filename = fullfile(downloadFolder,'cifar-10-matlab.tar.gz');

dataFolder = fullfile(downloadFolder,'cifar-10-batches-mat');
if ~exist(dataFolder,'dir')
    fprintf("Downloading CIFAR-10 dataset (175 MB)... ");
    websave(filename,url);
    untar(filename,downloadFolder);
    fprintf("Done.\n")
end
Convert the data to numeric arrays using the helper function loadCIFARData, which is used in the example Train Residual Network for Image Classification. To access this function, open the example as a live script.
[XTrain,YTrain,XValidation,YValidation] = loadCIFARData(downloadFolder);

For an example showing how to process this data for deep learning, see Train Residual Network for Image Classification.

Image classification

MathWorks® Merch

The MathWorks Merch data set is a small data set containing 75 images of MathWorks merchandise, belonging to five different classes (cap, cube, playing cards, screwdriver, and torch). You can use this data set to try out transfer learning and image classification quickly.

The images are of size 227-by-227-by-3.

Extract the MathWorks Merch data set.

filename = 'MerchData.zip';

dataFolder = fullfile(tempdir,'MerchData');
if ~exist(dataFolder,'dir')
    unzip(filename,tempdir);
end

Load the data as an image datastore using the imageDatastore function and specify the folder containing the image data.

imds = imageDatastore(dataFolder, ...
    'IncludeSubfolders',true,'LabelSource','foldernames');

For examples showing how to process this data for deep learning, see Get Started with Transfer Learning and Retrain Neural Network to Classify New Images.

Image classification

CamVid

The CamVid data set is a collection of images containing street-level views obtained from cars being driven [8]. The data set is useful for training networks that perform semantic segmentation of images and provides pixel-level labels for 32 semantic classes, including car, pedestrian, and road.

The images are of size 720-by-960-by-3.

Download and extract the CamVid data set from http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/. The data set is about 573 MB. Set downloadFolder to the location of the data.

downloadFolder = tempdir;
url = "http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData"
urlImages = url + "/files/701_StillsRaw_full.zip";
urlLabels = url + "/data/LabeledApproved_full.zip";

dataFolder = fullfile(downloadFolder,'CamVid');
dataFolderImages = fullfile(dataFolder,'images');
dataFolderLabels = fullfile(dataFolder,'labels');

filenameLabels = fullfile(dataFolder,'labels.zip');
filenameImages = fullfile(dataFolder,'images.zip');

if ~exist(filenameLabels, 'file') || ~exist(imagesZip,'file')   
    mkdir(dataFolder)
    
    fprintf("Downloading CamVid data set images (557 MB)... ");
    websave(filenameImages, urlImages);       
    unzip(filenameImages, dataFolderImages);
    fprintf("Done.\n")
   
    fprintf("Downloading CamVid data set labels (16 MB)... ");
    websave(filenameLabels, urlLabels);
    unzip(filenameLabels, dataFolderLabels);
    fprintf("Done.\n")
end

Load the data as a pixel label datastore using the pixelLabelDatastore function and specify the folder containing the label data, the classes, and the label IDs. To make training easier, group some of the original classes to reduce the number of classes from 32 to 11. To get the label IDs, use the helper function camvidPixelLabelIDs, which is used in the example Semantic Segmentation Using Deep Learning. To access this function, open the example as a live script.

imds = imageDatastore(dataFolderImages,'IncludeSubfolders',true);

classes = ["Sky" "Building" "Pole" "Road" "Pavement" "Tree" ...
    "SignSymbol" "Fence" "Car" "Pedestrian" "Bicyclist"];

labelIDs = camvidPixelLabelIDs;

pxds = pixelLabelDatastore(dataFolderLabels,classes,labelIDs);

For an example showing how to process this data for deep learning, see Semantic Segmentation Using Deep Learning.

Semantic segmentation

Vehicle

The Vehicle data set consists of 295 images containing one or two labeled instances of a vehicle. This small data set is useful for exploring the YOLO-v2 training procedure, but in practice, more labeled images are needed to train a robust detector.

The images are of size 720-by-960-by-3.

Extract the Vehicle data set. Set dataFolder to the location of the data.

filename = 'vehicleDatasetImages.zip';

dataFolder = fullfile(tempdir,'vehicleImages');
if ~exist(dataFolder,'dir')
    unzip(filename,tempdir);
end

Load the data set as a table of file names and bounding boxes from the extracted MAT file and convert the file names to absolute file paths.

data = load('vehicleDatasetGroundTruth.mat');
vehicleDataset = data.vehicleDataset;

vehicleDataset.imageFilename = fullfile(tempdir,vehicleDataset.imageFilename);

Create an image datastore containing the images and a box label datastore containing the bounding boxes using the imageDatastore and boxLabelDatastore functions, respectively. Combine the resulting datastores using the combine function.

filenamesImages = vehicleDataset.imageFilename;
tblBoxes = vehicleDataset(:,'vehicle');

imds = imageDatastore(filenamesImages);
blds = boxLabelDatastore(tblBoxes);

cds = combine(imds,blds);

For an example showing how to process this data for deep learning, see Object Detection Using YOLO v2 Deep Learning.

Object detection

RIT-18

Aerial photograph of Hamlin Beach State Park with colored pixel label overlay that indicates regions of grass, trees, sandy beach, asphalt, and other classes

The RIT-18 data set contains image data captured by a drone over Hamlin Beach State Park in New York state [9]. The data contains labeled training, validation, and test sets, with 18 object class labels including road markings, tree, and building. The data set is about 3 GB.

Download the RIT-18 data set from https://home.cis.rit.edu/~cnspci/other/data/rit18_data.mat. Set downloadFolder to the location of the data.

downloadFolder = tempdir;
url = 'https://home.cis.rit.edu/~cnspci/other/data/rit18_data.mat';
filename = fullfile(downloadFolder,'rit18_data.mat');

if ~exist(filename,'file')
    fprintf("Downloading Hamlin Beach data set (3 GB)... ");
    websave(filename,url);
    fprintf("Done.\n")
end

For an example showing how to process this data for deep learning, see Semantic Segmentation of Multispectral Images Using Deep Learning.

Semantic segmentation

BraTS

Axial slice of human brain with colored pixel label overlay that indicates regions of normal tissue and tumor tissue

The BraTS data set contains MRI scans of brain tumors, namely gliomas, which are the most common primary brain malignancies [10].

The data set contains 750 4-D volumes, each representing a stack of 3-D images. Each 4-D volume is of size 240-by-240-by-155-by-4, where the first three dimensions correspond to the height, width, and depth of a 3-D volumetric image. The fourth dimension corresponds to different scan modalities. The data set is divided into 484 training volumes with voxel labels and 266 test volumes. The data set is about 7 GB.

Create a directory to store the BraTS data set.

dataFolder = fullfile(tempdir,'BraTS');

if ~exist(dataFolder,'dir')
    mkdir(dataFolder);
end

Download the BraTS data from Medical Segmentation Decathlon by clicking the "Download Data" link. Download the "Task01_BrainTumour.tar" file.

Extract the TAR file into the directory specified by the dataFolder variable. If the extraction is successful, then dataFolder contains a directory named Task01_BrainTumour that has three subdirectories: imagesTr, imagesTs, and labelsTr.

For an example showing how to process this data for deep learning, see 3-D Brain Tumor Segmentation Using Deep Learning.

Semantic segmentation

Camelyon16

Six patches of normal tissue samples

The data from the Camelyon16 challenge contains a total of 400 whole-slide images (WSIs) of lymph nodes from two independent sources, separated into 270 training images and 130 test images [11]. The data set has a size of about 451 GB.

The training data set consists of 159 WSIs of normal lymph nodes and 111 WSIs of lymph nodes with tumor and healthy tissue. Usually, the tumor tissue is a small fraction of the healthy tissue. Ground truth coordinates of the lesion boundaries accompany the tumor images.

Create folders to store the Camelyon16 data set.

dataDir = fullfile(tempdir,"Camelyon16","training");
trainNormalDir = fullfile(dataDir,"normal");
trainTumorDir = fullfile(dataDir,"tumor");
trainAnnotationDir = fullfile(dataDir,"lesion_annotations");

if ~exist(dataDir,"dir")
    mkdir(dataDir);
    mkdir(trainNormalDir);
    mkdir(trainTumorDir);
    mkdir(trainAnnotationDir);
end

To download the entire training data set, go to http://gigadb.org/dataset/100439. Navigate to the Files tab and click (FTPSite), which is the FTP server location of the data set. Use an FTP client or call the ftp function to download the training data folder /pub/gigadb/pub/10.5524/100001_101000/100439/CAMELYON16/training from the host ftp.cngb.org. Download the contents of this training data folder by following these steps:

  • Download the lesion_annotations.zip file. Extract the files to the folder specified by the trainAnnotationDir variable.

  • Download the images from the normal subfolder to the folder specified by the trainNormalDir variable.

  • Download the images from the tumor subfolder to the folder specified by the trainTumorDir variable.

To download and access each WSI file individually, select files to download at the bottom of http://gigadb.org/dataset/100439 in the Files table.

For an example that shows how to process this data for deep learning, see Preprocess Multiresolution Images for Training Classification Network (Image Processing Toolbox).

Image classification (large images)

Low Dose CT Grand Challenge

Pair of noisy low-dose and high quality regular-dose CT images of the chest.

The Low Dose CT Grand Challenge includes pairs of regular-dose CT images and simulated low-dose CT images for 99 head scans (labeled N for neuro), 100 chest scans (labeled C for chest), and 100 abdomen scans (labeled L for liver) [12] [13]. The full data set is about 1.2 TB.

Create a directory to store the chest files from the Low Dose CT Grand Challenge data set.

dataDir = fullfile(tempdir,"LDCT","LDCT-and-Projection-data");
if ~exist(dataDir,'dir')
    mkdir(dataDir);
end

To download the data, go to The Cancer Imaging Archive website. Download the chest files from the "Images (DICOM, 952 GB)" data set using the NBIA Data Retriever. Specify the dataDir variable as the location of the downloaded data. When the data is downloaded successfully, dataDir contains 50 subfolders with names such as "C002" and "C004", ending with "C296".

For an example showing how to process this data for deep learning, see Unsupervised Medical Image Denoising Using CycleGAN.

Image-to-image regression

Common Objects in Context (COCO)

(Representative example)

The COCO 2014 train images data set consists of 82,783 images. The annotations data contains at least five captions corresponding to each image.

Create directories to store the COCO data set.

dataFolder = fullfile(tempdir,"coco");
if ~exist(dataFolder,'dir')
    mkdir(dataFolder);
end

Download and extract the COCO 2014 train images and captions from https://cocodataset.org/#download by clicking the "2014 Train images" and "2014 Train/Val annotations" links, respectively. Save the data in the folder specified by dataFolder.

Extract the captions from the file captions_train2014.json using the jsondecode function.

filename = fullfile(dataFolder,"annotations_trainval2014","annotations", ...
    "captions_train2014.json");
str = fileread(filename);
data = jsondecode(str);

The annotations field of the structure contains the data required for image captioning.

For an example showing how to process this data for deep learning, see Image Captioning Using Attention.

Image captioning

IAPR TC-12

A wall and gardens of the Alcazar royal palace in Seville, Spain

(Representative example)

The IAPR TC-12 Benchmark consists of 20,000 still natural images [14]. The data set includes photos of people, animals, cities, and more. The data file is about 1.8 GB.

Download the IAPR TC-12 data set. 

dataDir = fullfile(tempdir,'iaprtc12');
url = 'http://www-i6.informatik.rwth-aachen.de/imageclef/resources/iaprtc12.tgz';

if ~exist(dataDir,'dir')
    fprintf('Downloading IAPR TC-12 data set (1.8 GB)...\n');
    try
        untar(url,dataDir);
    catch 
        % On some Windows machines, the untar command throws an error for .tgz
        % files. Rename to .tg and try again.
        fileName = fullfile(tempdir,'iaprtc12.tg');
        websave(fileName,url);
        untar(fileName,dataDir);
    end
    fprintf('Done.\n\n');
end

Load the data as an image datastore using the imageDatastore function. Specify the folder containing the image data and the image file extensions.

imageDir = fullfile(dataDir,'images')
exts = {'.jpg','.bmp','.png'};
imds = imageDatastore(imageDir, ...
    'IncludeSubfolders',true,'FileExtensions',exts);

For an example showing how to process this data for deep learning, see Increase Image Resolution Using Deep Learning.

Image-to-image regression

Zurich RAW to RGB

Pair of RAW and RGB image patches of a street scene in Zurich

The Zurich RAW to RGB data set contains 48,043 spatially registered pairs of RAW and RGB training image patches of size 448-by-448 [15]. The data set contains two separate test sets. One test set consists of 1,204 spatially registered pairs of RAW and RGB image patches of size 448-by-448. The other test set consists of unregistered full-resolution RAW and RGB images. The data set is 22 GB.

Create a directory to store the Zurich RAW to RGB data set.

imageDir = fullfile(tempdir,'ZurichRAWToRGB');
if ~exist(imageDir,'dir')
    mkdir(imageDir);
end
To download the data set, request access using the Zurich RAW to RGB dataset form. Extract the data into the directory specified by the imageDir variable. If the extraction is successful, then imageDir contains three directories: full_resolution, test, and train.

For an example showing how to process this data for deep learning, see Develop Camera Processing Pipeline Using Deep Learning.

Image-to-image regression

See-In-The-Dark (SID)

Pair of severely underexposed and well-exposed images

The See-In-The-Dark (SID) data set provides registered pairs of RAW images of the same scene [16]. In each pair, one image has a short exposure time and is underexposed, and the other image has a longer exposure time and is well-exposed. The size of the Sony camera data from the SID data set is 25 GB.

Specify dataDir as the desired location of the data.

dataDir = fullfile(tempdir,"SID");
if ~exist(dataDir,"dir")
    mkdir(dataDir);
end

To download the data set, go to this link: https://storage.googleapis.com/isl-datasets/SID/Sony.zip. Extract the data into the directory specified by the dataDir variable. When extracted successfully, dataDir contains the directory Sony with two subdirectories: long and short. The files in the long subdirectory have a long exposure and are well-exposed. The files in the short subdirectory have a short exposure and are quite underexposed and dark.

The data set also provides text files that describe how to partition the files into training, validation, and test data sets. Move the files "Sony_train_list.txt", "Sony_val_list.txt", and "Sony_test_list.txt" to the directory specified by the dataDir variable.

For an example showing how to process this data for deep learning, see Brighten Extremely Dark Images Using Deep Learning.

Image-to-image regression

LIVE In the Wild

Three images of varying quality, with mean and standard devation of subjective quality scores

The LIVE In the Wild data set consists of 1,162 photos captured by mobile devices, with seven additional training images [17]. Each image is rated by an average of 175 individuals on a scale of [1, 100]. The data set provides the mean and standard deviation of the subjective scores for each image.

Specify imageDir as the desired location of the data.

imageDir = fullfile(tempdir,"LIVEInTheWild");
if ~exist(imageDir,'dir')
    mkdir(imageDir);
end

Download the data set by following the instructions outlined in LIVE In the Wild Image Quality Challenge Database. Extract the data into the directory specified by the imageDir variable. When extracted successfully, imageDir contains two directories: Data and Images.

For an example showing how to process this data for deep learning, see Quantify Image Quality Using Neural Image Assessment.

Image classification

Concrete Crack Images for Classification

Images of concrete without and with cracks.

The Concrete Crack Images for Classification data set contains images of two classes: "Negative" images without cracks present in the road and "Positive" images with cracks [18] [19]. The data set provides 20,000 images of each class. The size of the data set is 235 MB.

Specify dataDir as the desired location of the data.

dataDir = fullfile(tempdir,"ConcreteCracks");
if ~exist(dataDir,"dir")
    mkdir(dataDir);
end

To download the data set, go to this link: Concrete Crack Images for Classification. Extract the ZIP file to obtain a RAR file, then extract the contents of the RAR file into the directory specified by the dataDir variable. If the extraction is successful, then dataDir contains two subdirectories: Negative and Positive.

For an example showing how to process this data for deep learning, see Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings.

Image classification

MIR WM-811K (Wafer Defect Maps)

Images of wafer maps showing four types of defects

The Wafer Defect Map data set consists of 811,457 wafer map images, including 172,950 labeled images [20] [21]. Each image has only three pixel values. The value 0 indicates the background, the value 1 represents correctly behaving dies, and the value 2 represents defective dies. The labeled images have one of nine labels based on the spatial pattern of defects. The size of the data set is 3.5 GB.

Specify dataDir as the desired location of the data, then download the Wafer Defect Map data set.

dataDir = fullfile(tempdir,"WaferDefects");
dataURL = "http://mirlab.org/dataSet/public/MIR-WM811K.zip";
dataMatFile = fullfile(dataDir,"MIR-WM811K","MATLAB","WM811K.mat");

if exist(dataMatFile,"file") ~= 2
    unzip(dataURL,dataDir);
end

The data is stored in a MAT file as an array of structures. Load the data set into the workspace.

waferData = load(dataMatFile);
waferData = waferData.data;

For an example showing how to process this data for deep learning, see Classify Anomalies on Wafer Defect Maps Using Deep Learning.

Image classification

PCB Defect

Image of a printed circuit board with three labeled bounding boxes that identify short circuit defects

The Printed Circuit Board (PCB) Defect data set contains 1,386 images of PCB elements with synthesized defects [22] [23]. The data has six types of defects: missing hole, mouse bite, open circuit, short, spur, and spurious copper. Each image contains multiple defects of the same category in different locations. The data set provides bounding box and coordinate information for every defect in every image. The size of the data set is 1.87 GB.

Specify dataDir as the desired location of the data, then download the PCB Defect data set.

dataDir = fullfile(tempdir,"PCBDefects");
imageDir = fullfile(dataDir,"PCB-DATASET-master");

if ~exist(imageDir,"dir")
    dataURL = "https://github.com/Ironbrotherstyle/PCB-DATASET/archive/refs/heads/master.zip";
    unzip(dataURL,dataDir);
    delete(fullfile(imageDir,"*.m"),fullfile(imageDir,"*.mlx"), ...
        fullfile(imageDir,"*.mat"),fullfile(imageDir,"*.md")); 
end

For an example showing how to process this data for deep learning, see Detect Defects on Printed Circuit Boards Using YOLOX Network (Computer Vision Toolbox).

Object detection

Visual Anomaly (VisA)

Image of two printed circuit boards from the VisA data set, with and without defects, respectively

The Visual Anomaly (VisA) data set consists of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 different object subsets in three domains [24][25]. Four of the subsets correspond to four different types of PCBs, containing transistors, capacitors, chips, and other components. The anomalous images in the test set contain various surface defects (such as scratches, dents, color spots or cracks), and structural defects (such as part misplacement or missing parts). The data set contains 5 to 20 images per defect type and some images contain multiple defects.

Download one of the four PCB data subsets for deep learning. This data subset contains train and test folders, which include the normal training images, and the normal and anomalous test images, respectively. The size of the data set is 1.9 GB.

Specify dataDir as the desired location of the data, then download the VisA data set subset.

imageDir = fullfile(tempdir,"VisA");
if ~exist(imageDir,"dir")
    dataURL = "https://ssd.mathworks.com/supportfiles/vision/data/VisA.zip";
    unzip(dataURL,dataDir);
    delete(fullfile(imageDir,"*.m"),fullfile(imageDir,"*.mlx"), ...
        fullfile(imageDir,"*.mat"),fullfile(imageDir,"*.md")); 
end

For an example that shows how to process this data for deep learning, see Localize Industrial Defects Using PatchCore Anomaly Detector (Computer Vision Toolbox).

Image classification

Pill Quality Control (Pill QC)

Images of a normal pill, a pill with dirt contamination, and a pill with chip defects

The Pill QC data set contains images of three classes: "normal" images without defects, "chip" images with chip defects in the pills, and "dirt" images with dirt contamination. The data set provides 149 normal images, 43 chip images, and 138 dirt images. The size of the data set is 3.57 MB.

Specify dataDir as the desired location of the data, then download the Pill QC data set.

dataDir = fullfile(tempdir,"PillDefects");
imageDir = fullfile(dataDir,"pillQC-main");

if ~exist(imageDir,"dir")
    unzip("https://github.com/matlab-deep-learning/pillQC/archive/refs/heads/main.zip",dataDir); 
end

Load the data as an image datastore using the imageDatastore function and specify the folder containing the image data.

imageDir = fullfile(dataDir,"pillQC-main","images");
imds = imageDatastore(imageDir,IncludeSubfolders=true,LabelSource="foldernames");

For an example showing how to process this data for deep learning, see Detect Image Anomalies Using Explainable FCDD Network.

Image classification

Breast Ultrasound Images (BUSI)

Ultrasound images of normal breast tissue, a benign tumor, and a malignant tumor

The Breast Ultrasound Images (BUSI) data set contains 2-D breast ultrasound images [26]. The data set contains 133 normal images, 487 images with benign tumors, and 210 images with malignant tumors. Each ultrasound image has a corresponding tumor mask image for training semantic segmentation networks. The tumor mask labels have been reviewed by clinical radiologists. The size of the data set is approximately 197 MB.

Download the BUSI data set from the MathWorks website.

zipFile = matlab.internal.examples.downloadSupportFile("image","data/Dataset_BUSI.zip");
filepath = fileparts(zipFile);
unzip(zipFile,filepath)

Load the data as an image datastore using the imageDatastore function and label each image as normal, benign, or malignant according to the name of its folder.

imageDir = fullfile(filepath,"Dataset_BUSI_with_GT");
imds = imageDatastore(imageDir,IncludeSubfolders=true,LabelSource="foldernames");

For an example showing how to process this data for deep learning, see Breast Tumor Segmentation from Ultrasound Using Deep Learning.

Semantic segmentation

Child and Adolescent NeuroDevelopment Initiative (CANDI) neuroimaging data set

MRI image of the brain and ground truth segmentation mask of brain structures

The CANDI data set (subset HC_001) contains one brain MRI image volume and its corresponding segmentation label image [27]. The total size of the data set is approximately 2.5 MB.

Download the CANDI data set from the MathWorks website.

zipFile = matlab.internal.examples.downloadSupportFile("image","data/brainSegData.zip");
filepath = fileparts(zipFile);
unzip(zipFile,filepath)
dataDir = fullfile(filepath,"brainSegData");

For an example showing how to load and process this data for deep learning, see Brain MRI Segmentation Using Pretrained 3-D U-Net Network

Semantic segmentation

Sunnybrook Cardiac Data set

MRI image of the heart and ground truth segmentation mask of the left ventricle

The Sunnybrook Cardiac Data set contains cine MRI images and ground truth labels of the left ventricle [28]. The data set contains images from multiple patients with various cardiac pathologies. The MRI images are in the DICOM file format and the label images are in the PNG file format.

This code downloads a subset of the original data set from the MathWorks website. The subset contains MRI images and label images from 45 patients. The total download size is approximately 105 MB.

zipFile = matlab.internal.examples.downloadSupportFile("medical","CardiacMRI.zip");
filepath = fileparts(zipFile);
unzip(zipFile,filepath)

The imageDir folder contains the downloaded and unzipped data set.

imageDir = fullfile(filepath,"Cardiac MRI");

For an example showing how to process this data for deep learning, see Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network.

Semantic segmentation

Time Series and Signal Data Sets

DataDescriptionTask

Japanese Vowels

The Japanese Vowels data set contains preprocessed sequences representing utterances of Japanese vowels from different speakers [29] [30].

XTrain and XTest are cell arrays containing sequences of dimension 12 of varying length. TTrain and TTest are categorical vectors of labels 1 to 9, that correspond to the nine speakers. The entries in XTrain are matrices with 12 rows (one row for each feature) and varying numbers of columns (one column for each time step). XTest is a cell array containing 370 sequences of dimension 12 of varying length.

Load the Japanese Vowels training and test data sets as in-memory cell arrays containing numeric sequences using load JapaneseVowelsTrainData and load JapaneseVowelsTestData, respectively.

load JapaneseVowelsTrainData
load JapaneseVowelsTestData

For an example showing how to process this data for deep learning, see Sequence Classification Using Deep Learning.

Sequence-to-label classification

Chickenpox

The Chickenpox data set contains a single time series, with time steps corresponding to months and values corresponding to the number of cases. The output is a cell array, where each element is a single time step.

Load the Chickenpox data as a single numeric sequences using the chickenpox_dataset function. Reshape the data to be a row vector.

data = chickenpox_dataset;
data = [data{:}];

For an example showing how to process this data for deep learning, see Time Series Forecasting Using Deep Learning.

Time-series forecasting

Human Activity

The Human Activity data set contains seven time series of sensor data obtained from a smartphone worn on the body. Each sequence has three features and varies in length. The three features correspond to accelerometer readings in three different directions.

Load the Human Activity data set.

dataTrain = load('HumanActivityTrain');
dataTest = load('HumanActivityTest');

XTrain = dataTrain.XTrain;
YTrain = dataTrain.YTrain;
XTest = dataTest.XTest;
YTest = dataTest.YTest;

For an example showing how to process this data for deep learning, see Sequence-to-Sequence Classification Using Deep Learning.

Sequence-to-sequence classification

Turbofan Engine Degradation Simulation

Each time series of the Turbofan Engine Degradation Simulation data set represents a different engine [31]. Each engine starts with unknown degrees of initial wear and manufacturing variation. The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure.

The data contains a ZIP-compressed text files with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, and each column is a different variable. The columns correspond to the following:

  • Column 1 – Unit number

  • Column 2 – Time in cycles

  • Columns 3–5 – Operational settings

  • Columns 6–26 – Sensor measurements 1–21

Create a directory to store the Turbofan Engine Degradation Simulation data set.

dataFolder = fullfile(tempdir,"turbofan");
if ~exist(dataFolder,'dir')
    mkdir(dataFolder);
end

Download and extract the Turbofan Engine Degradation Simulation data set.

filename = matlab.internal.examples.downloadSupportFile( ...
    "nnet","data/TurbofanEngineDegradationSimulationData.zip");
unzip(filename,dataFolder)

Load the training and test data using the helper functions processTurboFanDataTrain and processTurboFanDataTest, respectively. These functions are used in the example Sequence-to-Sequence Regression Using Deep Learning. To access these functions, open the example as a live script.

filenamePredictors = fullfile(dataFolder,"train_FD001.txt");
[XTrain,YTrain] = processTurboFanDataTrain(filenamePredictors);

filenamePredictors = fullfile(dataFolder,"test_FD001.txt");
filenameResponses = fullfile(dataFolder,"RUL_FD001.txt");
[XTest,YTest] = processTurboFanDataTest(filenamePredictors,filenameResponses);

For an example showing how to process this data for deep learning, see Sequence-to-Sequence Regression Using Deep Learning.

Sequence-to-sequence regression, predictive maintenance

PhysioNet 2017 Challenge

The PhysioNet 2017 Challenge data set consists of a set of electrocardiogram (ECG) recordings sampled at 300 Hz and divided by a group of experts into different classes [33].

Download and extract the PhysioNet 2017 Challenge data set using the ReadPhysionetData script, which is used in the example Classify ECG Signals Using Long Short-Term Memory Networks. To access this function, open the example as a live script. The data set is about 95 MB.

ReadPhysionetData
data = load('PhysionetData.mat')
signals = data.Signals;
labels = data.Labels;

For an example showing how to process this data for deep learning, see Classify ECG Signals Using Long Short-Term Memory Networks.

Sequence-to-label classification

Tennessee Eastman Process (TEP) simulation

This data set consists of MAT files converted from the Tennessee Eastman Process (TEP) simulation data [32].

Download the Tennessee Eastman Process (TEP) simulation data set from the MathWorks support files site (see disclaimer). The data set has four components: fault-free training, fault-free testing, faulty training, and faulty testing. Download each file separately.

The data set is 1.7 GB.

fprintf("Downloading TEP faulty training data (613 MB)... ")
filenameFaultyTrain = matlab.internal.examples.downloadSupportFile('predmaint', ...
    'chemical-process-fault-detection-data/faultytraining.mat'); 
fprintf("Done.\n")

fprintf("Downloading TEP faulty testing data (1 GB)... ")
filenameFaultyTest = matlab.internal.examples.downloadSupportFile('predmaint', ...
    'chemical-process-fault-detection-data/faultytesting.mat');
fprintf("Done.\n")

fprintf("Downloading TEP fault-free training data (36 MB)... ")
filenameFaultFreeTrain = matlab.internal.examples.downloadSupportFile('predmaint', ...
    'chemical-process-fault-detection-data/faultfreetraining.mat'); 
fprintf("Done.\n")

fprintf("Downloading TEP fault-free testing data (69 MB)... ")
filenameFaultFreeTest = matlab.internal.examples.downloadSupportFile('predmaint', ...
    'chemical-process-fault-detection-data/faultfreetesting.mat'); 
fprintf("Done.\n")

Load the downloaded files into the MATLAB® workspace.

load(filenameFaultyTrain);
load(filenameFaultyTest);
load(filenameFaultFreeTrain);
load(filenameFaultFreeTest);

For an example showing how to process this data for deep learning, see Chemical Process Fault Detection Using Deep Learning.

Sequence-to-label classification

PhysioNet ECG Segmentation

The PhysioNet ECG Segmentation data set consists of roughly 15 minutes of ECG recordings from a total of 105 patients [33] [34]. To obtain each recording, the examiners placed two electrodes on different locations on a patient's chest, resulting in a two-channel signal. The database provides signal region labels generated by an automated expert system.

Download the PhysioNet ECG Segmentation data set from the https://github.com/mathworks/physionet_ECG_segmentation by downloading the ZIP file QT_Database-master.zip. The data set is 72 MB. Set downloadFolder to the location of the data.

downloadFolder = tempdir;

url = "https://github.com/mathworks/physionet_ECG_segmentation/raw/master/QT_Database-master.zip";
filename = fullfile(downloadFolder,"QT_Database-master.zip");

dataFolder = fullfile(downloadFolder,"QT_Database-master");

if ~exist(dataFolder,"dir")
    fprintf("Downloading Physionet ECG Segmentation data set (72 MB)... ")
    websave(filename,url);
    unzip(filename,downloadFolder);
    fprintf("Done.\n")
end

Unzipping creates the folder QT_Database-master in your temporary directory. This folder contains the text file README.md and the following files:

  • QTData.mat

  • Modified_physionet_data.txt

  • License.txt

QTData.mat contains the PhysioNet ECG Segmentation data. The file Modified_physionet_data.txt provides the source attributions for the data and a description of the operations applied to each raw ECG recording. Load the PhysioNet ECG Segmentation data from the MAT file.

load(fullfile(dataFolder,'QTData.mat'))

For an example showing how to process this data for deep learning, see Waveform Segmentation Using Deep Learning.

Sequence-to-label classification, waveform segmentation

Synthetic pedestrian, car, and bicyclist backscattering

Generate a synthetic pedestrian, car, and bicyclist backscattering data set using the helper functions helperBackScatterSignals and helperDopplerSignatures, which are used in the example Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox).

The helper function helperBackScatterSignals generates a specified number of pedestrian, bicyclist, and car radar returns. For each realization, the return signals have dimensions Nfast-by-Nslow, where Nfast is the number of fast-time samples and Nslow is the number of slow-time samples.

The helper function helperDopplerSignatures computes the short-time Fourier transform (STFT) of a radar return to generate the micro-Doppler signature. To obtain the micro-Doppler signatures, use the helper functions to apply the STFT and a preprocessing method to each signal.

To access these functions, open the example as a live script.

numPed = 1; % Number of pedestrian realizations
numBic = 1; % Number of bicyclist realizations
numCar = 1; % Number of car realizations
[xPedRec,xBicRec,xCarRec,Tsamp] = helperBackScatterSignals(numPed,numBic,numCar);

[SPed,T,F] = helperDopplerSignatures(xPedRec,Tsamp);
[SBic,~,~] = helperDopplerSignatures(xBicRec,Tsamp);
[SCar,~,~] = helperDopplerSignatures(xCarRec,Tsamp);

For an example showing how to process this data for deep learning, see Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox).

Sequence-to-label classification

Generated waveforms

Generate rectangular, linear FM, and phase coded waveforms using the helper function helperGenerateRadarWaveforms, which is used in the example Radar and Communications Waveform Classification Using Deep Learning (Radar Toolbox).

The helper function helperGenerateRadarWaveforms generates 3000 signals with a sample rate of 100 MHz for each modulation type using phased.RectangularWaveform for rectangular pulses, phased.LinearFMWaveform for linear FM, and phased.PhaseCodedWaveform for phase-coded pulses with Barker code.

To access these functions, open the example as a live script.

[wav, modType] = helperGenerateRadarWaveforms;

For an example showing how to process this data for deep learning, see Radar and Communications Waveform Classification Using Deep Learning (Radar Toolbox).

Sequence-to-label classification

Video Data Sets

DataDescriptionTask

HMDB: a large human motion database

(Representative example)

The HMBD51 data set contains about 2 GB of video data for 7000 clips from 51 classes, such as drink, run, and pushup.

Download and extract the HMBD51 data set from HMDB: a large human motion database. The data set is about 2 GB.

After you extract the RAR files, get the file names and the labels of the videos by using the helper function hmdb51Files, which used in the example Classify Videos Using Deep Learning. Set dataFolder to the location of the data. To access this function, open the example as a live script.

dataFolder = fullfile(tempdir,"hmdb51_org");
[files,labels] = hmdb51Files(dataFolder);

For an example showing how to process this data for deep learning, see Classify Videos Using Deep Learning.

Video classification

Text Data Sets

DataDescriptionTask

Factory Reports

The Factory Reports data set is a table containing approximately 500 reports with various attributes including a plain text description in the variable Description and a categorical label in the variable Category.

Read the Factory Reports data from the file "factoryReports.csv". Extract the text data and the labels from the Description and Category columns, respectively.

filename = "factoryReports.csv";
data = readtable(filename,'TextType','string');

textData = data.Description;
labels = data.Category;

For an example showing how to process this data for deep learning, see Classify Text Data Using Deep Learning.

Text classification, topic modeling

Shakespeare's Sonnets

The file sonnets.txt contains all of Shakespeare's sonnets in a single text file.

Read the Shakespeare's Sonnets data from the file "sonnets.txt".

filename = "sonnets.txt";
textData = fileread(filename);

The sonnets are indented by two whitespace characters and are separated by two newline characters. Remove the indentations using replace and split the text into separate sonnets using split. Remove the main title from the first three elements and the sonnet titles, which appear before each sonnet.

textData = replace(textData,"  ","");
textData = split(textData,[newline newline]);
textData = textData(5:2:end);

For an example showing how to process this data for deep learning, see Generate Text Using Deep Learning.

Topic modeling, text generation

ArXiv Metadata

The ArXiv API allows you to access the metadata of scientific e-prints submitted to https://arxiv.org including the abstract and subject areas. For more information, see https://arxiv.org/help/api.

Import a set of abstracts and category labels from math papers using the arXiV API.

url = "https://export.arxiv.org/oai2?verb=ListRecords" + ...
    "&set=math" + ...
    "&metadataPrefix=arXiv";
options = weboptions('Timeout',160);
code = webread(url,options);

For an example showing how to parse the returned XML code and import more records, see Multilabel Text Classification Using Deep Learning.

Text classification, topic modeling

Books from Project Gutenberg

You can download many books from Project Gutenberg. For example, download the text from Alice's Adventures in Wonderland by Lewis Carroll from https://www.gutenberg.org/files/11/11-h/11-h.htm using the webread function.

url = "https://www.gutenberg.org/files/11/11-h/11-h.htm";
code = webread(url);

The HTML code contains the relevant text inside <p> (paragraph) elements. Extract the relevant text by parsing the HTML code using the htmlTree function and then finding all the elements with the element name "p".

tree = htmlTree(code);
selector = "p";
subtrees = findElement(tree,selector);

Extract the text data from the HTML subtrees using the extractHTMLText function and remove the empty elements.

textData = extractHTMLText(subtrees);
textData(textData == "") = [];

For an example showing how to process this data for deep learning, see Word-By-Word Text Generation Using Deep Learning.

Topic modeling, text generation

Weekend updates

The file weekendUpdates.xlsx contains example social media status updates containing the hashtags "#weekend" and "#vacation". This data set requires Text Analytics Toolbox™.

Extract the text data from the file weekendUpdates.xlsx using the readtable function and extract the text data from the variable TextData.

filename = "weekendUpdates.xlsx";
tbl = readtable(filename,'TextType','string');
textData = tbl.TextData;

For an example showing how to process this data, see Analyze Sentiment in Text (Text Analytics Toolbox).

Sentiment analysis

Roman Numerals

The CSV file "romanNumerals.csv" contains the decimal numbers 1–1000 in the first column and the corresponding Roman numerals in the second column.

Load the decimal-Roman numeral pairs from the CSV file "romanNumerals.csv".

filename = fullfile("romanNumerals.csv");

options = detectImportOptions(filename, ...
    'TextType','string', ...
    'ReadVariableNames',false);
options.VariableNames = ["Source" "Target"];
options.VariableTypes = ["string" "string"];

data = readtable(filename,options);

For an example showing how to process this data for deep learning, see Sequence-to-Sequence Translation Using Attention.

Sequence-to-sequence translation

Finance Reports

The Securities and Exchange Commission (SEC) allows you to access financial reports via the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) API. For more information, see https://www.sec.gov/os/accessing-edgar-data.

To download this data, use the function financeReports attached to the example Generate Domain Specific Sentiment Lexicon (Text Analytics Toolbox) as a supporting file. To access this function, open the example as a Live Script.

year = 2019;
qtr = 4;
maxLength = 2e6;
textData = financeReports(year,qtr,maxLength);

For an example showing how to process this data, see Generate Domain Specific Sentiment Lexicon (Text Analytics Toolbox).

Sentiment analysis

Audio Data Sets

DataDescriptionTask

Speech Commands

The Speech Commands data set consists of approximately 65,000 audio files labeled with 1 of 12 classes including yes, no, on, and off, as well as classes corresponding to unknown commands and background noise [35].

Download and extract the Speech Commands data set from https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz. The data set is about 1.4 GB.

Set dataFolder to the location of the data. Use audioDatastore to create a datastore that contains the file names and the corresponding labels.

dataFolder = tempdir;
ads = audioDatastore(dataFolder, ...
    'IncludeSubfolders',true, ...
    'FileExtensions','.wav', ...
    'LabelSource','foldernames');

For an example showing how to process this data for deep learning, see Train Speech Command Recognition Model Using Deep Learning.

Audio classification, speech recognition

Mozilla Common Voice

The Mozilla Common Voice data set consists of audio recordings of speech and corresponding text files. The data also includes demographic metadata such as age and accent.

Download and extract the Mozilla Common Voice data set data set from https://commonvoice.mozilla.org/. The data set is an open data set, which means that it can grow over time. As of October 2019, the data set is about 28 GB. Set dataFolder to the location of the data. Use audioDatastore to create a datastore that contains the file names and the corresponding labels.

dataFolder = tempdir;
ads = audioDatastore(fullfile(dataFolder,"clips"));

Audio classification, speech recognition.

Free Spoken Digit Dataset

The Free Spoken Digit Dataset, as of January 29, 2019, consists of 2000 recordings of the English digits 0 through 9 obtained from four speakers. Two of the speakers in this version are native speakers of American English and two speakers are nonnative speakers of English with a Belgium French and German accent respectively. The data is sampled at 8000 Hz.

Download the Free Spoken Digit Dataset (FSDD) recordings from https://github.com/Jakobovski/free-spoken-digit-dataset.

Set dataFolder to the location of the data. Use audioDatastore to create a datastore that contains the file names and the corresponding labels.

dataFolder = fullfile(tempdir,'free-spoken-digit-dataset','recordings');
ads = audioDatastore(dataFolder);

For an example showing how to process this data for deep learning, see Spoken Digit Recognition with Wavelet Scattering and Deep Learning.

Audio classification, speech recognition.

Berlin Database of Emotional Speech

The Berlin Database of Emotional Speech contains 535 utterances spoken by 10 actors intended to convey one of the following emotions: anger, boredom, disgust, anxiety/fear, happiness, sadness, or neutral [36]. The emotions are text independent.

The file names are codes indicating the speaker ID, text spoken, emotion, and version. The website contains a key for interpreting the code and additional information about the speakers such as age.

Download the Berlin Database of Emotional Speech from http://emodb.bilderbar.info/index-1280.html. The data set is about 40 MB.

Set dataFolder to the location of the data. Use audioDatastore to create a datastore that contains the file names and the corresponding labels.

dataFolder = tempdir;
ads = audioDatastore(fullfile(dataFolder,"wav"));

For an example showing how to process this data for deep learning, see Speech Emotion Recognition.

Audio classification, speech recognition.

TUT Acoustic Scenes 2017

The TUT Acoustic Scenes 2017 data set consists of 10-second audio segments from 15 acoustic scenes including bus, car, and library.

Download and extract the TUT Acoustic Scenes 2017 data set from TUT Acoustic scenes 2017, Development dataset and TUT Acoustic scenes 2017, Evaluation dataset [37].

For an example showing how to process this data for deep learning, see Acoustic Scene Recognition Using Late Fusion.

Acoustic scene classification

Point Cloud Data Sets

DataDescriptionTask

WPI Lidar Data

The WPI Lidar data is collected using an Ouster OS1 sensor. It contains organized lidar point cloud scans of highway scenes and corresponding ground truth labels for car and truck objects.

The data set has 1617 point clouds stored as pointCloud (Computer Vision Toolbox) objects in a cell array. The size of the data file is approximately 760 MB.

Execute this code to download the data set. 

url = 'https://www.mathworks.com/supportfiles/lidar/data/WPI_LidarData.tar.gz';

outputFolder = fullfile(tempdir,'WPI');
lidarDataTarFile = fullfile(outputFolder,'WPI_LidarData.tar.gz');

if ~exist(lidarDataTarFile, 'file') 
    mkdir(outputFolder);
    
    disp('Downloading WPI Lidar driving data (760 MB)...');
    websave(lidarDataTarFile, url);
    untar(lidarDataTarFile,outputFolder); 
end

lidarData = load(fullfile(outputFolder, 'WPI_LidarData.mat'));
Depending on your internet connection, the download process can take some time. Alternatively, you can download the data set directly to your local disk from your web browser using the URL, and extract the WPI_LidarData folder. If you do so, change the outputFolder variable in the code to the location of the downloaded file.

For an example showing how to process this data for deep learning, see Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network.

Semantic segmentation

PandaSet Data

PandaSet contains 2560 organized lidar point cloud scans of various city scenes captured using the Pandar 64 sensor. The data set provides semantic segmentation labels for 12 different classes and 3-D bounding box information for three classes, which are car, truck, and pedestrian. The size of the data set is 5.2 GB.

Execute this code to download the data set.

url = 'https://ssd.mathworks.com/supportfiles/lidar/data/Pandaset_LidarData.tar.gz';
outputFolder = fullfile(tempdir,'Pandaset');
lidarDataTarFile = fullfile(outputFolder,'Pandaset_LidarData.tar.gz');
if ~exist(lidarDataTarFile, 'file')
    mkdir(outputFolder);
    disp('Downloading Pandaset Lidar driving data (5.2 GB)...');
    websave(lidarDataTarFile, url);
    untar(lidarDataTarFile,outputFolder);
end

lidarData =  fullfile(outputFolder,'Lidar');
labelsFolder = fullfile(outputFolder,'semanticLabels');

Depending on your internet connection, the download process can take some time. Alternatively, you can download the data set to your local disk from your web browser using the URL, and then extract the Pandaset_LidarData folder. If you do so, change the outputFolder variable in the code to the location of the downloaded file.

For examples showing how to process this data for deep learning, see Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network and Lidar 3-D Object Detection Using PointPillars Deep Learning.

Object detection, Semantic segmentation

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