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

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"
}