create_classification_project {AzureVision}R Documentation

Create, retrieve, update and delete Azure Custom Vision projects


Create, retrieve, update and delete Azure Custom Vision projects


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)


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, ...)



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).


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".


What formats are supported when exporting the model.


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.


An optional text description of the project.


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


Further arguments passed to lower-level methods.


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:

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


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


## 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")


get_project(endp, "myproject")

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

## End(Not run)

[Package AzureVision version 1.0.2 Index]