Vision provides pretrained image analysis AI models
that let you to find and tag objects, text, and entire scenes in images.
Pretrained models let you use AI with no data science experience. Provide an image to the
Vision service and get back information about the objects, text, scenes, and any faces in the
image without needing to create your own model.
Use Cases
Here are several use cases for pretrained image analysis models.
Digital asset management
Tag digital media-like images for better indexing and retrieval.
Scene monitoring
Detect if items are on retail shelves, vegetation is growing in the surveillance image
of a power line, or if trucks are available at a lot for delivery or shipment.
Face detection
Privacy: Hide identities by adding a blur to the image using face location
information returned through the face detection feature.
Prerequisite for Biometrics: Use the facial quality score to decide if a face is
clear and unobstructed.
Digital asset management: Tag images with facial information for better indexing
and retrieval.
Supported Formats
Vision supports several image analysis formats.
Images can be uploaded either from local storage or Oracle Cloud Infrastructure Object Storage. The images can be in the following
formats:
JPG
PNG
Pretrained Models
Vision has four types of pretrained image analysis
model.
Object detection is used to find and identity objects within an image. For example, if
you have an image of a living room, Vision find the objects
there, such as a chair, a sofa, and a TV. It then provides bounding boxes for each of the
objects and identifies them.
Vision provides a confidence score for each object
identified. The confidence score is a decimal number. Scores closer to 1 indicate a higher
confidence in the objects classification, while lower scores indicate a lower confidence
score. The range of the confidence score for each label is from 0 to 1.
Image classification can be used to identify scene-based features and objects in an
image. You can have one classification or many classifications, depending on the use case and
the number of items in an image. For example, if you have an image of a person running, Vision identifies the person, the clothing, and the
footwear.
Vision provides a confidence score for each label. The
confidence score is a decimal number. Scores closer to 1 indicate a higher confidence in the
label, while lower scores indicate lower confidence score. The range of the confidence score
for each label is from 0 to 1.
Vision can detect and recognize text in a
document.
Language classification identifies the language of a document, then OCR draws bounding boxes
around the printed or hand-written text it finds in an image, and digitizes the text. For
example, if you have an image of a stop sign, Vision finds the
text in that image and extracts the text STOP. It provides bounding boxes for
the identified text.
Vision provides a confidence score for each text grouping.
The confidence score is a decimal number. Scores closer to 1 indicate a higher confidence in
the extracted text, while lower scores indicate lower confidence score. The range of the
confidence score for each label is from 0 to 1.
Text Detection can be used with Document AI or Image Analysis models.
OCR support is limited to English. If you know the text in your images is in English, set the
language to Eng.
Vision provides pretrained models for customers to
extract insights about their images without needing Data Scientists.
You need the following before using a pretrained model:
A paid tenancy account in Oracle Cloud Infrastructure.
Familiarity with Oracle Cloud Infrastructure Object Storage.
You can call the pretrained Image Analysis models as a batch request using Rest APIs,
SDK, or CLI. You can call the pretrained Image Analysis models as a single request using
the Console, Rest APIs, SDK, or CLI.
See the Limits section for information on what is allowed in batch
requests.
