CV Onboarding#

This page shows the basics of setting up computer vision (CV) models and onboarding to the Arthur system to monitor vision-specific performance.

Getting Started#

The first step is to import functions from the arthurai package and establish a connection with Arthur.

# Arthur imports
from arthurai import ArthurAI
from arthurai.common.constants import InputType, OutputType, Stage

arthur = ArthurAI(url="", 

Registering a CV Model#

Each computer vision model is created with input_type = InputType.Image and with specified width and height dimensions for the processed images. Here, we register a classification model on 1024x1024 images:

arthur_cv_model = arthur.model(name="ImageQuickstart",


You can send images to the Arthur platform with any dimensions, and we’ll keep the original you send as well as a resized copy in the model dimensions. If you enable explainability for your model, the resized versions will be passed to it to generate explanations.

The different OutputType values currently supported for computer vision models are classification, regression, and object detection.

Formatting Data#

Computer vision models require the same structure as Tabular and NLP models. However, the attribute value for Image attributes should be a valid path to the image file for that inference.

Here is an example of a valid reference_data dataframe to build an ArthurModel with:

    image_attr             pred_value   ground_truth    non_input_1   
0   'img_path/img_0.png'   0.1          0               0.2  
1   'img_path/img_1.png'   0.05         0              -0.3 
2   'img_path/img_2.png'   0.02         1               0.7     ...
3   'img_path/img_3.png'   0.8          1               1.2 
4   'img_path/img_4.png'   0.4          0              -0.5  

Non-Input Attributes#

Any non-pixel features to be tracked in images for performance comparison or bias detection should be added as non-input attributes. For example, any metadata about the identities of people captured in images for a CV model should be included as non-input attributes.

Reviewing the Model Schema#

Before you call, you can call the model schema to check that your data is parsed correctly.

For an image model, the model schema should look like this:

     name           stage                 value_type    categorical   is_unique  
0    image_attr     PIPELINE_INPUT        IMAGE         False         True   
1    pred_value     PREDICTED_VALUE       FLOAT         False         False      ...
2    ground_truth   GROUND_TRUTH          INTEGER       True          False   
3    non_input_1    NON_INPUT_DATA        FLOAT         False         False   

Object Detection#

Formatting Bounding Boxes#

If using an Object Detection model, bounding boxes should be formatted as lists in the form:

[class_id, confidence, top_left_x, top_left_y, width, height]

The first two components of the bounding box list represent the classification being made within the bounding box. The class_id represents the ID of the class detected within the bounding box, and the confidence represents the % confidence the model has in this prediction (0.0 for completely unconfident and 1.0 for completely confident).

The next four components of the bounding box list represent the location of the bounding box within the image: the top_left_x and top_left_y represent the X and Y pixel coordinates of the top-left corner of the bounding box. These pixel coordinates are calculated from the origin, which is in the top left corner of the image. This means that each coordinate is calculated by counting pixels from the left of the image or the top of the image, respectively. The width represents the number of pixels the bounding box covers from left to right, and the height represents the number of pixels the bounding box covers from top to bottom.

So using the following model schema as an example:

	name	                stage	           value_type	
0	image_attr              PIPELINE_INPUT	   IMAGE	
1	label	                GROUND_TRUTH	   BOUNDING_BOX
2	objects_detected        PREDICTED_VALUE	   BOUNDING_BOX	

a valid dataset would look like

#    image_attr              objects_detected              ground_truth             non_input_1   
0,   'img_path/img_0.png',   [[0, 0.98, 12, 20, 50, 25],   [0, 1, 14, 22, 48, 29],  0.2
                             [1, 0.47, 92, 140, 80, 36]]     
1,   'img_path/img_1.png',   [[1, 0.22, 4, 5, 14, 32]]     [1, 1, 25, 43, 49, 25]   -0.3     #...
#                                ...

Finishing Onboarding#

Once you have finished formatting your reference data and your model schema looks correct using, you are finished locally configuring your model and its attributes - so you are ready to complete onboarding your model.

To finish onboarding your CV model, the following steps apply, which is the same for CV models as it is for models of any InputType and OutputType:

  1. Save your model

  2. Send inferences your model has made on historical data

    1. To confirm that the inferences have been sent, you can view your model and its inferences in the Arthur dashboard.

  3. Connect your production data and model inference pipeline to Arthur

To see an example of saving your model and sending inference data, see the Arthur Quickstart.

To see multiple examples of connecting production data and model inference pipeline to Arthur, see our Integrations.


For an overview of configuring enrichments for image models, see the enrichments guide.

For a step-by-step walkthrough of setting up the explainability Enrichment for image models, see CV Explainability.