Output data, returned as an M-by-2 cell array, cell
array of categorical matrices, or a table.
| Datastore | Output Description |
|---|
PixelLabelDatastore | Table with
MiniBatchSize number of rows. For
the last batch of data in the datastore,
numObservations must be divisible
by MiniBatchSize or
read returns a partial batch
containing all the remaining observations in the
datastore. |
PixelLabelImageDatastore |
boxLabelDatastore | N-by-2 or
N-by-3 cell matrix.
N must be less than or equal to
ReadSize(ds). The
first column can contain data, such as point cloud
data for point cloud detectors, or images for object
detectors. The second column must be a
cell vector that contains M-by-5
matrices of bounding boxes in the
format[xcenter,ycenter,width,height,yaw]. The
third column must be a cell vector that contains the
label names corresponding to each bounding box.
Label names are represented as an
M-by-1 categorical
vector. |
You can use the combine function to create a datastore to use for training.
imageDatastore —
Create a datastore containing images.
PixelLabelDatastore — Create a datastore
containing pixel data.
boxLabelDatastore — Create a datastore containing
bounding boxes and labels.
combine(imds,blds)
— Combine images, bounding boxes, and labels into one
datastore.
combine(pxds,blds)
— Combine pixel data, bounding boxes, and labels into one
datastore.
For more information, see Datastores for Deep Learning (Deep Learning Toolbox).