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

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

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"
}