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

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