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

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

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"
}