RoboflowClassificationModel¶
Run inference on a multi-class classification model hosted on or uploaded to Roboflow.
You can query any model that is private to your account, or any public model available on Roboflow Universe.
You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
The unique name of this step.. | ❌ |
model_id |
str |
Roboflow model identifier. | ✅ |
confidence |
float |
Confidence threshold for predictions. | ✅ |
disable_active_learning |
bool |
Parameter to decide if Active Learning data sampling is disabled for the model. | ✅ |
active_learning_target_dataset |
str |
Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information. | ✅ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Check what blocks you can connect to RoboflowClassificationModel
.
- inputs:
RelativeStaticCrop
,MaskVisualization
,CircleVisualization
,BoundingBoxVisualization
,HaloVisualization
,EllipseVisualization
,PerspectiveCorrection
,CropVisualization
,PolygonVisualization
,PixelateVisualization
,BlurVisualization
,AbsoluteStaticCrop
,BackgroundColorVisualization
,CornerVisualization
,DynamicCrop
,ColorVisualization
,TriangleVisualization
,LabelVisualization
,DotVisualization
- outputs:
DetectionsClassesReplacement
,RoboflowDatasetUpload
,PropertyDefinition
The available connections depend on its binding kinds. Check what binding kinds
RoboflowClassificationModel
has.
Bindings
-
input
images
(Batch[image]
): The image to infer on.model_id
(roboflow_model_id
): Roboflow model identifier.confidence
(float_zero_to_one
): Confidence threshold for predictions.disable_active_learning
(boolean
): Parameter to decide if Active Learning data sampling is disabled for the model.active_learning_target_dataset
(roboflow_project
): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
-
output
predictions
(Batch[classification_prediction]
):'predictions'
key from Classification Model outputs.
Example JSON definition of RoboflowClassificationModel step
{
"name": "<your_step_name_here>",
"type": "RoboflowClassificationModel",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence": 0.3,
"disable_active_learning": true,
"active_learning_target_dataset": "my_project"
}