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

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

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"
}