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RoboflowInstanceSegmentationModel

Run inference on an instance segmentation 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 Unique name of step in workflows.
model_id str Roboflow model identifier.
class_agnostic_nms bool Value to decide if NMS is to be used in class-agnostic mode..
class_filter List[str] List of classes to retrieve from predictions (to define subset of those which was used while model training).
confidence float Confidence threshold for predictions.
iou_threshold float Parameter of NMS, to decide on minimum box intersection over union to merge boxes.
max_detections int Maximum number of detections to return.
max_candidates int Maximum number of candidates as NMS input to be taken into account..
mask_decode_mode str Parameter of mask decoding in prediction post-processing..
tradeoff_factor float Post-processing parameter to dictate tradeoff between fast and accurate.
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 RoboflowInstanceSegmentationModel.

The available connections depend on its binding kinds. Check what binding kinds RoboflowInstanceSegmentationModel has.

Bindings
  • input

    • images (Batch[image]): Reference an image to be used as input for step processing.
    • model_id (roboflow_model_id): Roboflow model identifier.
    • class_agnostic_nms (boolean): Value to decide if NMS is to be used in class-agnostic mode..
    • class_filter (list_of_values): List of classes to retrieve from predictions (to define subset of those which was used while model training).
    • confidence (float_zero_to_one): Confidence threshold for predictions.
    • iou_threshold (float_zero_to_one): Parameter of NMS, to decide on minimum box intersection over union to merge boxes.
    • max_detections (integer): Maximum number of detections to return.
    • max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
    • mask_decode_mode (string): Parameter of mask decoding in prediction post-processing..
    • tradeoff_factor (float_zero_to_one): Post-processing parameter to dictate tradeoff between fast and accurate.
    • 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

Example JSON definition of RoboflowInstanceSegmentationModel step
{
    "name": "<your_step_name_here>",
    "type": "RoboflowInstanceSegmentationModel",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "class_agnostic_nms": true,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "confidence": 0.3,
    "iou_threshold": 0.4,
    "max_detections": 300,
    "max_candidates": 3000,
    "mask_decode_mode": "accurate",
    "tradeoff_factor": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}