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

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"
}