Keypoint Detection Model

v2

Class: RoboflowKeypointDetectionModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.keypoint_detection.v2.RoboflowKeypointDetectionModelBlockV2

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 keypoint 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_keypoint_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..	✅
keypoint_confidence	float	Confidence threshold to predict a keypoint as visible..	✅
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 Keypoint Detection Model in version v2.

• inputs: Corner Visualization, Image Convert Grayscale, Label Visualization, Clip Comparison, Image Slicer, Image Blur, SIFT Comparison, Florence-2 Model, Google Gemini, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Camera Focus, Model Comparison Visualization, Stitch Images, Line Counter, Distance Measurement, Polygon Visualization, Detection Event Log, Stability AI Inpainting, Reference Path Visualization, OpenAI, Detections List Roll-Up, Slack Notification, Circle Visualization, Background Subtraction, Stability AI Image Generation, Roboflow Dataset Upload, Icon Visualization, VLM as Classifier, Pixel Color Count, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Color Visualization, Clip Comparison, Mask Visualization, Roboflow Dataset Upload, Anthropic Claude, Image Slicer, Template Matching, Buffer, Pixelate Visualization, OpenAI, Line Counter, Instance Segmentation Model, Keypoint Detection Model, Email Notification, Image Contours, Dynamic Zone, Google Gemini, JSON Parser, Text Display, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, Identify Outliers, VLM as Detector, Relative Static Crop, Detections Consensus, Camera Focus, Classification Label Visualization, Image Threshold, PTZ Tracking (ONVIF).md), Camera Calibration, Dot Visualization, Anthropic Claude, Background Color Visualization, Dimension Collapse, Polygon Zone Visualization, Keypoint Visualization, Grid Visualization, Dynamic Crop, Anthropic Claude, Trace Visualization, Crop Visualization, Absolute Static Crop, Line Counter Visualization, Florence-2 Model, Google Gemini, Twilio SMS Notification, Image Preprocessing, Identify Changes, SIFT, Perspective Correction, Email Notification, Motion Detection, Halo Visualization, VLM as Classifier, SIFT Comparison, Local File Sink, Depth Estimation, Morphological Transformation, Polygon Visualization, Single-Label Classification Model, QR Code Generator, OpenAI, Llama 3.2 Vision, VLM as Detector, Bounding Box Visualization, Size Measurement
• outputs: Corner Visualization, Label Visualization, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, SmolVLM2, Detections Consensus, Florence-2 Model, SAM 3, Ellipse Visualization, Webhook Sink, Single-Label Classification Model, Multi-Label Classification Model, Dot Visualization, Background Color Visualization, Qwen2.5-VL, Qwen3-VL, Keypoint Visualization, Model Comparison Visualization, Detections Filter, Detection Offset, Object Detection Model, Dynamic Crop, Keypoint Detection Model, Trace Visualization, Detections Transformation, Crop Visualization, Detections List Roll-Up, Circle Visualization, SAM 3, Roboflow Dataset Upload, Icon Visualization, Florence-2 Model, Segment Anything 2 Model, Moondream2, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Color Visualization, Instance Segmentation Model, Detections Merge, SAM 3, Roboflow Dataset Upload, Detections Classes Replacement, Pixelate Visualization, Byte Tracker, Single-Label Classification Model, Instance Segmentation Model, Keypoint Detection Model, Bounding Box Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Keypoint 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..
  • keypoint_confidence (float_zero_to_one): Confidence threshold to predict a keypoint as visible..
  • 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 (keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.
  • model_id (roboflow_model_id): Roboflow model id.

Example JSON definition of step Keypoint Detection Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_keypoint_detection_model@v2",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "keypoint_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: RoboflowKeypointDetectionModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.keypoint_detection.v1.RoboflowKeypointDetectionModelBlockV1

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 keypoint 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_keypoint_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..	✅
keypoint_confidence	float	Confidence threshold to predict a keypoint as visible..	✅
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 Keypoint Detection Model in version v1.

• inputs: Corner Visualization, Image Convert Grayscale, Label Visualization, Clip Comparison, Image Slicer, Image Blur, SIFT Comparison, Florence-2 Model, Google Gemini, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Camera Focus, Model Comparison Visualization, Stitch Images, Line Counter, Distance Measurement, Polygon Visualization, Detection Event Log, Stability AI Inpainting, Reference Path Visualization, OpenAI, Detections List Roll-Up, Slack Notification, Circle Visualization, Background Subtraction, Stability AI Image Generation, Roboflow Dataset Upload, Icon Visualization, VLM as Classifier, Pixel Color Count, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Color Visualization, Clip Comparison, Mask Visualization, Roboflow Dataset Upload, Anthropic Claude, Image Slicer, Template Matching, Buffer, Pixelate Visualization, OpenAI, Line Counter, Instance Segmentation Model, Keypoint Detection Model, Email Notification, Image Contours, Dynamic Zone, Google Gemini, JSON Parser, Text Display, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, Identify Outliers, VLM as Detector, Relative Static Crop, Detections Consensus, Camera Focus, Classification Label Visualization, Image Threshold, PTZ Tracking (ONVIF).md), Camera Calibration, Dot Visualization, Anthropic Claude, Background Color Visualization, Dimension Collapse, Polygon Zone Visualization, Keypoint Visualization, Grid Visualization, Dynamic Crop, Anthropic Claude, Trace Visualization, Crop Visualization, Absolute Static Crop, Line Counter Visualization, Florence-2 Model, Google Gemini, Twilio SMS Notification, Image Preprocessing, Identify Changes, SIFT, Perspective Correction, Email Notification, Motion Detection, Halo Visualization, VLM as Classifier, SIFT Comparison, Local File Sink, Depth Estimation, Morphological Transformation, Polygon Visualization, Single-Label Classification Model, QR Code Generator, OpenAI, Llama 3.2 Vision, VLM as Detector, Bounding Box Visualization, Size Measurement
• outputs: Corner Visualization, Label Visualization, Image Blur, Florence-2 Model, SIFT Comparison, Google Gemini, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Perception Encoder Embedding Model, Model Comparison Visualization, Line Counter, Detections Filter, Distance Measurement, Polygon Visualization, Detection Offset, Stability AI Inpainting, Reference Path Visualization, OpenAI, OpenAI, Detections List Roll-Up, Slack Notification, Circle Visualization, Stability AI Image Generation, Roboflow Dataset Upload, Path Deviation, Icon Visualization, LMM For Classification, YOLO-World Model, Pixel Color Count, Cache Get, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Color Visualization, Clip Comparison, Detections Merge, SAM 3, Mask Visualization, Roboflow Dataset Upload, Anthropic Claude, Time in Zone, Detections Classes Replacement, Line Counter, OpenAI, Pixelate Visualization, Byte Tracker, CLIP Embedding Model, Instance Segmentation Model, Email Notification, Google Gemini, Stitch OCR Detections, Text Display, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, Google Vision OCR, Detections Consensus, SAM 3, Classification Label Visualization, Image Threshold, LMM, PTZ Tracking (ONVIF).md), Dot Visualization, Anthropic Claude, Time in Zone, Stitch OCR Detections, Background Color Visualization, Seg Preview, Polygon Zone Visualization, Keypoint Visualization, Anthropic Claude, Dynamic Crop, Trace Visualization, SAM 3, Crop Visualization, Detections Transformation, Line Counter Visualization, Florence-2 Model, Time in Zone, Google Gemini, Detections Stitch, Segment Anything 2 Model, Moondream2, Twilio SMS Notification, Image Preprocessing, Instance Segmentation Model, Perspective Correction, Email Notification, Halo Visualization, Path Deviation, Local File Sink, Depth Estimation, CogVLM, Morphological Transformation, Polygon Visualization, OpenAI, QR Code Generator, Cache Set, Llama 3.2 Vision, Bounding Box Visualization, Size Measurement

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Keypoint 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..
  • keypoint_confidence (float_zero_to_one): Confidence threshold to predict a keypoint as visible..
  • 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 (keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.

Example JSON definition of step Keypoint Detection Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_keypoint_detection_model@v1",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "keypoint_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"
}