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

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

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"
}