Images and Labels

Images

● In the API, a dataset is represented by an object of the EClassificationDataset type.
● The supported image file types are:
□ PNG
□ TIFF
□ JPEG
□ BMP
□ J2K
● The supported Open eVision image object types are:
□ EImageType_BW8
□ EImageType_BW16
□ EImageType_C24
● The supported image size depends on the type of the deep learning tool.
□ EasyClassify and EasyLocate require that all images have the same size. Images that do not have the size configured for the tool are automatically resized before being processed by the neural network.
□ EasySegment splits images into patches. As such, the tools can process images of different size and the images are processed at their native resolution.
□ In all cases, your dataset should cover the variability of sizes that you want to process in production.

File formats

The supported standard file formats for the dataset and image annotation are:

● The COCO Json for EasySegment Supervised and EasyLocate Axis Aligned Bounding Box annotations.
□ A COCO json file contains the annotation for a dataset (several images).
□ In Deep Learning Studio, use the Import feature to import COCO datasets.
2 See https://cocodataset.org/#format-data for a description of the COCO Json dataset format.
● The YOLO TXT annotation format for EasyLocate Axis Aligned Bounding Box annotations.
□ For each image, the annotations are in a file with the same filename as the image and the .txt extension.
□ In Deep Learning Studio, if the annotation files are located in the same folder as their corresponding images, use the Add images feature to import the images and their annotations.
2 See https://pjreddie.com/darknet/yolo for the original YOLO source code.
● PASCAL VOC XML annotation for EasyLocate Axis Aligned Bounding Box annotations.
□ For each image, the annotations are in a file with the same filename as the image and the .xml extension.
□ In Deep Learning Studio, if the annotation files are located in the same folder as their corresponding images, use the Add images feature to import the images and their annotations.
2 See http://host.robots.ox.ac.uk/pascal/VOC for resources on the PASCAL VOC XML annotations.

Labels

● There are 3 types of labels:
□ The image labels represent a characteristic of an image and its content. Use them to annotate images for EasyClassify or EasySegment Unsupervised.
□ The segmentation labels represent a characteristics of pixels. Use them to annotate image pixels for EasySegment Supervised.
□ The object labels represent a characteristic of a region of an image delimited by a bounding box. Use them to annotate images for EasyLocate.
NOTE: Deep Learning Studio only displays the labels for the tool type of the project.
● Images have the following labeling states:
□ Labeled or Unlabeled if the image is or is not associated with an image label.

- Only labeled images are used to train an EasyClassify or an EasySegment Unsupervised tool.

- In the API, use EClassificationDataset::HasLabel(imageIndex).

□ With or without segmentation if the image has or has not a ground truth segmentation.

- Only images with segmentation are used to train an EasySegment Supervised tool.

- In the API, use EClassificationDataset::HasSegmenation(imageIndex).

□ With or without object labeling if the image has or has not a ground truth object labeling.

- Only images with object labeling are used to train an EasyLocate tool.

- In the API, use EClassificationDataset::HasObjectLabeling(imageIndex).

● The ground truth segmentation of an image has the following state:
□ Background when all the pixels of the image are associated with the Background segmentation label.

- In defect detection applications, a background segmentation means that the image contains no defect.

□ With foreground blobs when the segmentation contains at least one pixel associated with a segmentation label different from Background.

- In defect detection applications, a segmentation with foreground blobs means that the image contains defects.

- In the API, use EClassificationDataset::HasForegroundSegments(imageIndex).

● The ground truth object labeling of an image has the following state:
□ No objects when there is no object in the image.

- In defect detection applications, an image with no object means that the image contains no defect.

□ With objects when there is at least one object in the image.

- In defect detection applications, an image with objects means that the image contains defects.

- In the API, use EClassificationDataset::GetImageNumObjects(imageIndex) to determine if the image has objects or not.

● In Deep Learning Studio, the icons (visible) and (hidden) represent the visibility state of the images with the corresponding state and/or image label in the image list.
□ Click on these icons to toggle the visibility state.