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

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"
}