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

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

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"
}