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: QR Code Generator, Image Convert Grayscale, Google Gemini, Dynamic Crop, SIFT Comparison, Blur Visualization, SIFT, Bounding Box Visualization, Stability AI Outpainting, Identify Changes, Camera Focus, Slack Notification, Keypoint Visualization, Trace Visualization, Instance Segmentation Model, Polygon Visualization, Pixel Color Count, Ellipse Visualization, Model Comparison Visualization, Anthropic Claude, Dimension Collapse, OpenAI, Local File Sink, Triangle Visualization, Polygon Zone Visualization, Halo Visualization, Distance Measurement, Stability AI Image Generation, VLM as Detector, Florence-2 Model, Single-Label Classification Model, Circle Visualization, Email Notification, Google Gemini, Motion Detection, Clip Comparison, Camera Focus, Anthropic Claude, Perspective Correction, VLM as Classifier, Reference Path Visualization, Corner Visualization, Color Visualization, Twilio SMS/MMS Notification, Multi-Label Classification Model, Image Slicer, OpenAI, Camera Calibration, Image Blur, Buffer, Line Counter, VLM as Detector, Dot Visualization, Roboflow Custom Metadata, Image Threshold, Model Monitoring Inference Aggregator, Morphological Transformation, Label Visualization, Background Color Visualization, Classification Label Visualization, Keypoint Detection Model, Roboflow Dataset Upload, Mask Visualization, Dynamic Zone, Detections List Roll-Up, Pixelate Visualization, Absolute Static Crop, Line Counter, PTZ Tracking (ONVIF).md), Detections Consensus, Size Measurement, Webhook Sink, Grid Visualization, Contrast Equalization, Image Preprocessing, Google Gemini, Relative Static Crop, Stability AI Inpainting, Image Contours, Line Counter Visualization, Stitch Images, Template Matching, Crop Visualization, JSON Parser, OpenAI, Llama 3.2 Vision, Icon Visualization, Clip Comparison, SIFT Comparison, Identify Outliers, Depth Estimation, Twilio SMS Notification, VLM as Classifier, Florence-2 Model, Background Subtraction, Object Detection Model, Image Slicer, Roboflow Dataset Upload, Email Notification
• outputs: Corner Visualization, Color Visualization, Detections Combine, Detections Stitch, Multi-Label Classification Model, Stitch OCR Detections, Line Counter, SmolVLM2, Byte Tracker, Detections Transformation, Qwen3-VL, SAM 3, Roboflow Custom Metadata, Dot Visualization, Model Monitoring Inference Aggregator, Velocity, Object Detection Model, Dynamic Crop, Blur Visualization, Detection Offset, Label Visualization, Background Color Visualization, Bounding Box Visualization, Time in Zone, Byte Tracker, Roboflow Dataset Upload, Trace Visualization, Keypoint Detection Model, Instance Segmentation Model, Path Deviation, Detections List Roll-Up, Pixelate Visualization, PTZ Tracking (ONVIF).md), Ellipse Visualization, Line Counter, Model Comparison Visualization, Keypoint Detection Model, Detections Consensus, Size Measurement, Moondream2, Triangle Visualization, Webhook Sink, Qwen2.5-VL, SAM 3, Distance Measurement, Byte Tracker, Crop Visualization, Time in Zone, Path Deviation, Florence-2 Model, Single-Label Classification Model, Icon Visualization, Detections Stabilizer, Circle Visualization, Instance Segmentation Model, Single-Label Classification Model, Time in Zone, Florence-2 Model, Camera Focus, Detections Classes Replacement, Detections Filter, Detections Merge, Multi-Label Classification Model, Object Detection Model, Segment Anything 2 Model, SAM 3, Perspective Correction, Roboflow Dataset Upload, Overlap Filter

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: QR Code Generator, Image Convert Grayscale, Google Gemini, Dynamic Crop, SIFT Comparison, Blur Visualization, SIFT, Bounding Box Visualization, Stability AI Outpainting, Identify Changes, Camera Focus, Slack Notification, Keypoint Visualization, Trace Visualization, Instance Segmentation Model, Polygon Visualization, Pixel Color Count, Ellipse Visualization, Model Comparison Visualization, Anthropic Claude, Dimension Collapse, OpenAI, Local File Sink, Triangle Visualization, Polygon Zone Visualization, Halo Visualization, Distance Measurement, Stability AI Image Generation, VLM as Detector, Florence-2 Model, Single-Label Classification Model, Circle Visualization, Email Notification, Google Gemini, Motion Detection, Clip Comparison, Camera Focus, Anthropic Claude, Perspective Correction, VLM as Classifier, Reference Path Visualization, Corner Visualization, Color Visualization, Twilio SMS/MMS Notification, Multi-Label Classification Model, Image Slicer, OpenAI, Camera Calibration, Image Blur, Buffer, Line Counter, VLM as Detector, Dot Visualization, Roboflow Custom Metadata, Image Threshold, Model Monitoring Inference Aggregator, Morphological Transformation, Label Visualization, Background Color Visualization, Classification Label Visualization, Keypoint Detection Model, Roboflow Dataset Upload, Mask Visualization, Dynamic Zone, Detections List Roll-Up, Pixelate Visualization, Absolute Static Crop, Line Counter, PTZ Tracking (ONVIF).md), Detections Consensus, Size Measurement, Webhook Sink, Grid Visualization, Contrast Equalization, Image Preprocessing, Google Gemini, Relative Static Crop, Stability AI Inpainting, Image Contours, Line Counter Visualization, Stitch Images, Template Matching, Crop Visualization, JSON Parser, OpenAI, Llama 3.2 Vision, Icon Visualization, Clip Comparison, SIFT Comparison, Identify Outliers, Depth Estimation, Twilio SMS Notification, VLM as Classifier, Florence-2 Model, Background Subtraction, Object Detection Model, Image Slicer, Roboflow Dataset Upload, Email Notification
• outputs: Cache Get, Detections Stitch, Detections Combine, Seg Preview, QR Code Generator, Byte Tracker, YOLO-World Model, Google Gemini, Velocity, Dynamic Crop, Blur Visualization, Detection Offset, Bounding Box Visualization, Stability AI Outpainting, Slack Notification, Keypoint Visualization, Trace Visualization, Instance Segmentation Model, Polygon Visualization, Cache Set, Pixel Color Count, Ellipse Visualization, OpenAI, Model Comparison Visualization, Anthropic Claude, Local File Sink, Triangle Visualization, SAM 3, Polygon Zone Visualization, Halo Visualization, Distance Measurement, LMM, Byte Tracker, Stability AI Image Generation, Time in Zone, Path Deviation, CLIP Embedding Model, Florence-2 Model, Circle Visualization, Email Notification, Detections Stabilizer, Google Vision OCR, Google Gemini, Clip Comparison, Detections Classes Replacement, Anthropic Claude, Camera Focus, Detections Filter, Instance Segmentation Model, Perspective Correction, Perception Encoder Embedding Model, Reference Path Visualization, Corner Visualization, Color Visualization, Twilio SMS/MMS Notification, OpenAI, Stitch OCR Detections, Line Counter, Image Blur, Detections Transformation, SAM 3, Dot Visualization, Roboflow Custom Metadata, Image Threshold, Model Monitoring Inference Aggregator, Morphological Transformation, Label Visualization, Background Color Visualization, Time in Zone, Classification Label Visualization, Byte Tracker, Roboflow Dataset Upload, Path Deviation, Mask Visualization, Detections List Roll-Up, Pixelate Visualization, PTZ Tracking (ONVIF).md), Line Counter, Size Measurement, Moondream2, Detections Consensus, Webhook Sink, Image Preprocessing, Contrast Equalization, Google Gemini, Stability AI Inpainting, OpenAI, Line Counter Visualization, Crop Visualization, OpenAI, Llama 3.2 Vision, Icon Visualization, SIFT Comparison, Depth Estimation, Twilio SMS Notification, Time in Zone, Florence-2 Model, LMM For Classification, SAM 3, Detections Merge, Segment Anything 2 Model, CogVLM, Roboflow Dataset Upload, Overlap Filter, Email Notification

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"
}