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