Create an image dataset for object detection
Create a dataset from images for object detection. Depending on the storage format specified, this dataset can be used for Caffe or TensorFlow models.
About this task
Before creating an LMDB dataset for the purposes of object detection, make sure that your training data resides on the shared file system. The training data must be in one folder which contains two sub folders, one for .jpg images named JPEGImages and one for annotations named Annotations.
Each image must have a corresponding annotation of the same name, for example: 01_01.jpg resides in the /training-images/JPEGImages folder and 01_01.xml resides in the /training-images/Annotations folder.
<annotation>
<folder>JPEGImages</folder>
<filename>./JPEGImages/01_01.jpg</filename>
<path>/JPEGImages/</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>2000</width>
<height>2000</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>mitoses</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>935</xmin>
<ymin>880</ymin>
<xmax>1015</xmax>
<ymax>960</ymax>
</bndbox>
</object>
</annotation>
There are open source tools that can help with creating this relationship, for example, LabelImg.
Procedure
Results
The dataset is created once it is in Created state. If creation failed, see the driver and executor logs in the Spark Applications tab.
What to do next
To view details about the dataset, click the dataset name. To use the dataset in a training run, either create a training model or start a training run.