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

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"
}