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

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

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"
}