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