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

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"
}