Object Detection Model¶

Version `v1`¶

Run inference on a object-detection 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.

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/roboflow_object_detection_model@v1to add the block as as step in your workflow.

Properties¶

Name	Type	Description	Refs
`name`	`str`	The unique name of this step..	❌
`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..	✅
`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 Object Detection Model in version v1.

The available connections depend on its binding kinds. Check what binding kinds Object Detection Model in version v1 has.

Bindings

input
- images (image): The image to infer on.
- 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..
- 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
- inference_id (string): String value.
- predictions (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.

Example JSON definition of step Object Detection Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_object_detection_model@v1",
    "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,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}

Object Detection Model¶

Version v1¶

Type identifier¶

Properties¶

Available Connections¶

Version `v1`¶