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

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"
}