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

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"
}