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

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