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

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

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"
}