Object Detection Model

v2

Class: RoboflowObjectDetectionModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.object_detection.v2.RoboflowObjectDetectionModelBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

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@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
model_id	str	Roboflow model identifier..	✅
confidence	float	Confidence threshold for predictions..	✅
class_filter	List[str]	List of accepted classes. Classes must exist in the model's training set..	✅
iou_threshold	float	Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..	✅
max_detections	int	Maximum number of detections to return..	✅
class_agnostic_nms	bool	Boolean flag to specify if NMS is to be used in class-agnostic mode..	✅
max_candidates	int	Maximum number of candidates as NMS input to be taken into account..	✅
disable_active_learning	bool	Boolean flag to disable project-level active learning for this block..	✅
active_learning_target_dataset	str	Target dataset for active learning, if enabled..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Object Detection Model in version v2.

• inputs: Size Measurement, Roboflow Custom Metadata, Buffer, Image Convert Grayscale, VLM as Detector, Absolute Static Crop, Distance Measurement, Relative Static Crop, Line Counter Visualization, Background Color Visualization, Camera Focus, Image Contours, Image Slicer, Reference Path Visualization, SIFT Comparison, Triangle Visualization, Line Counter, Multi-Label Classification Model, Depth Estimation, Llama 3.2 Vision, Roboflow Dataset Upload, Dynamic Zone, Clip Comparison, Perspective Correction, Object Detection Model, Crop Visualization, Identify Changes, Webhook Sink, Dot Visualization, Model Comparison Visualization, Email Notification, Classification Label Visualization, Camera Calibration, Instance Segmentation Model, Dimension Collapse, Slack Notification, Stability AI Image Generation, Trace Visualization, Line Counter, Corner Visualization, Image Threshold, Blur Visualization, Local File Sink, Stability AI Inpainting, Keypoint Detection Model, SIFT, Circle Visualization, JSON Parser, OpenAI, Florence-2 Model, Twilio SMS Notification, Label Visualization, Stitch Images, Image Preprocessing, Template Matching, Grid Visualization, Polygon Zone Visualization, Keypoint Visualization, Bounding Box Visualization, Image Blur, Halo Visualization, Google Gemini, Ellipse Visualization, Color Visualization, Pixelate Visualization, Single-Label Classification Model, SIFT Comparison, Pixel Color Count, VLM as Detector, Roboflow Dataset Upload, Polygon Visualization, VLM as Classifier, Image Slicer, Clip Comparison, Model Monitoring Inference Aggregator, Mask Visualization, Anthropic Claude, Identify Outliers, Florence-2 Model, VLM as Classifier, Dynamic Crop, Detections Consensus
• outputs: Size Measurement, Detection Offset, Roboflow Custom Metadata, Path Deviation, Moondream2, Florence-2 Model, Multi-Label Classification Model, Distance Measurement, Label Visualization, Detections Classes Replacement, Background Color Visualization, Detections Stitch, Keypoint Detection Model, Instance Segmentation Model, Byte Tracker, Object Detection Model, Triangle Visualization, SmolVLM2, Path Deviation, Line Counter, Detections Stabilizer, Detections Transformation, Multi-Label Classification Model, Byte Tracker, Stitch OCR Detections, Roboflow Dataset Upload, Bounding Box Visualization, Perspective Correction, Object Detection Model, Ellipse Visualization, Color Visualization, Crop Visualization, Webhook Sink, Dot Visualization, Pixelate Visualization, Detections Filter, Single-Label Classification Model, Model Comparison Visualization, Instance Segmentation Model, Qwen2.5-VL, Detections Merge, Velocity, Overlap Filter, Roboflow Dataset Upload, Segment Anything 2 Model, Time in Zone, Line Counter, Time in Zone, Trace Visualization, Corner Visualization, Single-Label Classification Model, Blur Visualization, Model Monitoring Inference Aggregator, Keypoint Detection Model, Florence-2 Model, Circle Visualization, Byte Tracker, Dynamic Crop, Detections Consensus

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Object Detection Model in version v2 has.

Bindings

• input

  • images (image): The image to infer on..
  • model_id (roboflow_model_id): Roboflow model identifier..
  • confidence (float_zero_to_one): Confidence threshold for predictions..
  • class_filter (list_of_values): List of accepted classes. Classes must exist in the model's training set..
  • iou_threshold (float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
  • max_detections (integer): Maximum number of detections to return..
  • class_agnostic_nms (boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..
  • max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
  • disable_active_learning (boolean): Boolean flag to disable project-level active learning for this block..
  • active_learning_target_dataset (roboflow_project): Target dataset for active learning, if enabled..

• output

  • inference_id (inference_id): Inference identifier.
  • predictions (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
  • model_id (roboflow_model_id): Roboflow model id.

Example JSON definition of step Object Detection Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_object_detection_model@v2",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "iou_threshold": 0.4,
    "max_detections": 300,
    "class_agnostic_nms": true,
    "max_candidates": 3000,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}

v1

Class: RoboflowObjectDetectionModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.object_detection.v1.RoboflowObjectDetectionModelBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

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	Enter a unique identifier for this step..	❌
model_id	str	Roboflow model identifier..	✅
confidence	float	Confidence threshold for predictions..	✅
class_filter	List[str]	List of accepted classes. Classes must exist in the model's training set..	✅
iou_threshold	float	Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..	✅
max_detections	int	Maximum number of detections to return..	✅
class_agnostic_nms	bool	Boolean flag to specify if NMS is to be used in class-agnostic mode..	✅
max_candidates	int	Maximum number of candidates as NMS input to be taken into account..	✅
disable_active_learning	bool	Boolean flag to disable project-level active learning for this block..	✅
active_learning_target_dataset	str	Target dataset for active learning, if enabled..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Object Detection Model in version v1.

• inputs: Size Measurement, Roboflow Custom Metadata, Buffer, Image Convert Grayscale, VLM as Detector, Absolute Static Crop, Distance Measurement, Relative Static Crop, Line Counter Visualization, Background Color Visualization, Camera Focus, Image Contours, Image Slicer, Reference Path Visualization, SIFT Comparison, Triangle Visualization, Line Counter, Multi-Label Classification Model, Depth Estimation, Llama 3.2 Vision, Roboflow Dataset Upload, Dynamic Zone, Clip Comparison, Perspective Correction, Object Detection Model, Crop Visualization, Identify Changes, Webhook Sink, Dot Visualization, Model Comparison Visualization, Email Notification, Classification Label Visualization, Camera Calibration, Instance Segmentation Model, Dimension Collapse, Slack Notification, Stability AI Image Generation, Trace Visualization, Line Counter, Corner Visualization, Image Threshold, Blur Visualization, Local File Sink, Stability AI Inpainting, Keypoint Detection Model, SIFT, Circle Visualization, JSON Parser, OpenAI, Florence-2 Model, Twilio SMS Notification, Label Visualization, Stitch Images, Image Preprocessing, Template Matching, Grid Visualization, Polygon Zone Visualization, Keypoint Visualization, Bounding Box Visualization, Image Blur, Halo Visualization, Google Gemini, Ellipse Visualization, Color Visualization, Pixelate Visualization, Single-Label Classification Model, SIFT Comparison, Pixel Color Count, VLM as Detector, Roboflow Dataset Upload, Polygon Visualization, VLM as Classifier, Image Slicer, Clip Comparison, Model Monitoring Inference Aggregator, Mask Visualization, Anthropic Claude, Identify Outliers, Florence-2 Model, VLM as Classifier, Dynamic Crop, Detections Consensus
• outputs: Size Measurement, LMM, Roboflow Custom Metadata, Detection Offset, Distance Measurement, Line Counter Visualization, Detections Classes Replacement, Background Color Visualization, Reference Path Visualization, Instance Segmentation Model, SIFT Comparison, Triangle Visualization, Path Deviation, Line Counter, Detections Stabilizer, Detections Transformation, Byte Tracker, Llama 3.2 Vision, Roboflow Dataset Upload, Google Vision OCR, Clip Comparison, Perspective Correction, Crop Visualization, Webhook Sink, Dot Visualization, Detections Filter, Email Notification, Model Comparison Visualization, Classification Label Visualization, Instance Segmentation Model, Slack Notification, Stability AI Image Generation, Cache Set, Detections Merge, Time in Zone, Line Counter, Time in Zone, Corner Visualization, Trace Visualization, Image Threshold, Local File Sink, Blur Visualization, CogVLM, Stability AI Inpainting, Circle Visualization, Overlap Filter, OpenAI, Path Deviation, Florence-2 Model, Twilio SMS Notification, Label Visualization, Image Preprocessing, Detections Stitch, Byte Tracker, Polygon Zone Visualization, Keypoint Visualization, LMM For Classification, Stitch OCR Detections, Bounding Box Visualization, Image Blur, OpenAI, CLIP Embedding Model, Google Gemini, Halo Visualization, Ellipse Visualization, Color Visualization, Pixelate Visualization, Pixel Color Count, YOLO-World Model, Velocity, Roboflow Dataset Upload, Polygon Visualization, Segment Anything 2 Model, Cache Get, Model Monitoring Inference Aggregator, Mask Visualization, Anthropic Claude, Florence-2 Model, Byte Tracker, Dynamic Crop, Detections Consensus

Input and Output Bindings

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..
  • confidence (float_zero_to_one): Confidence threshold for predictions..
  • class_filter (list_of_values): List of accepted classes. Classes must exist in the model's training set..
  • iou_threshold (float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
  • max_detections (integer): Maximum number of detections to return..
  • class_agnostic_nms (boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..
  • max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
  • disable_active_learning (boolean): Boolean flag to disable project-level active learning for this block..
  • active_learning_target_dataset (roboflow_project): Target dataset for active learning, if enabled..

• 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",
    "confidence": 0.3,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "iou_threshold": 0.4,
    "max_detections": 300,
    "class_agnostic_nms": true,
    "max_candidates": 3000,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}