create_classification_project {AzureVision}

Create, retrieve, update and delete Azure Custom Vision projects

Description

Create, retrieve, update and delete Azure Custom Vision projects

Usage

create_classification_project(endpoint, name, domain = "general",
  export_target = c("none", "standard", "vaidk"), multiple_tags = FALSE,
  description = NULL)

create_object_detection_project(endpoint, name, domain = "general",
  export_target = c("none", "standard", "vaidk"), description = NULL)

list_projects(endpoint)

get_project(endpoint, name = NULL, id = NULL)

update_project(endpoint, name = NULL, id = NULL, domain = "general",
  export_target = c("none", "standard", "vaidk"), multiple_tags = FALSE,
  description = NULL)

delete_project(object, ...)

Arguments

endpoint

A custom vision endpoint.

name, id

The name and ID of the project. At least one of these must be specified for get_project, update_project and delete_project. The name is required for create_project (the ID will be assigned automatically).

domain

What kinds of images the model is meant to apply to. The default "general" means the model is suitable for use in a generic setting. Other, more specialised domains for classification include "food", "landmarks" and "retail"; for object detection the other possible domain is "logo".

export_target

What formats are supported when exporting the model.

multiple_tags

For classification models, Whether multiple categories (tags/labels) for an image are allowed. The default is FALSE, meaning an image represents one and only one category. Ignored for object detection models.

description

An optional text description of the project.

object

For delete_customvision_project, either an endpoint, or a project object.

...

Further arguments passed to lower-level methods.

Details

A Custom Vision project contains the metadata for a model: its intended purpose (classification vs object detection), the domain, the set of training images, and so on. Once you have created a project, you upload images to it, and train models based on those images. A trained model can then be published as a predictive service, or exported for standalone use.

By default, a Custom Vision project does not support exporting the model; this allows it to be more complex, and thus potentially more accurate. Setting export_target="standard" enables exporting to the following formats:

• ONNX 1.2

• CoreML, for iOS 11 devices

• TensorFlow

• TensorFlow Lite, for Android devices

• A Docker image for the Windows, Linux or Raspberry Pi 3 (ARM) platform

Setting export_target="vaidk" allows exporting to Vision AI Development Kit format, in addition to the above.

Value

delete_project returns NULL invisibly, on a successful deletion. The others return an object of class customvision_project.

See Also

customvision_training_endpoint, add_images, train_model, publish_model, predict.customvision_model, do_training_op

• CustomVision.ai: An interactive site for building Custom Vision models, provided by Microsoft

• Training API reference

• Prediction API reference

Examples

## Not run: 

endp <- customvision_training_endpoint(url="endpoint_url", key="key")

create_classification_project(endp, "myproject")
create_classification_project(endp, "mymultilabelproject", multiple_tags=TRUE)
create_object_detection_project(endp, "myobjdetproj")

create_classification_project(endp, "mystdproject", export_target="standard")

list_projects(endp)

get_project(endp, "myproject")

update_project(endp, "myproject", export_target="vaidk")


## End(Not run)