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: Line Counter, Detections Consensus, Llama 3.2 Vision, Blur Visualization, Dimension Collapse, Perspective Correction, Polygon Zone Visualization, Bounding Box Visualization, QR Code Generator, Pixelate Visualization, Distance Measurement, Trace Visualization, Roboflow Custom Metadata, Image Threshold, Polygon Visualization, Dynamic Crop, Icon Visualization, Image Slicer, Identify Outliers, Stability AI Outpainting, Model Comparison Visualization, Single-Label Classification Model, Dynamic Zone, Clip Comparison, OpenAI, Classification Label Visualization, Stitch Images, Size Measurement, Mask Visualization, Florence-2 Model, Relative Static Crop, Absolute Static Crop, SIFT Comparison, Google Gemini, Circle Visualization, Florence-2 Model, Ellipse Visualization, Image Convert Grayscale, Image Preprocessing, Color Visualization, Image Blur, Stability AI Image Generation, Anthropic Claude, Keypoint Visualization, Camera Calibration, Local File Sink, Keypoint Detection Model, VLM as Detector, Image Slicer, Line Counter, Email Notification, VLM as Detector, Roboflow Dataset Upload, Background Color Visualization, Triangle Visualization, Slack Notification, Multi-Label Classification Model, Object Detection Model, Halo Visualization, Corner Visualization, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Dot Visualization, Image Contours, Twilio SMS Notification, VLM as Classifier, Reference Path Visualization, Morphological Transformation, Motion Detection, OpenAI, Webhook Sink, PTZ Tracking (ONVIF).md), Instance Segmentation Model, Contrast Equalization, Camera Focus, Stability AI Inpainting, JSON Parser, Clip Comparison, Line Counter Visualization, Identify Changes, Template Matching, Email Notification, Crop Visualization, Grid Visualization, VLM as Classifier, Buffer, SIFT, Depth Estimation, Background Subtraction, Label Visualization, Anthropic Claude, Pixel Color Count, SIFT Comparison, OpenAI
• outputs: Detections Consensus, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Blur Visualization, Dot Visualization, SAM 3, Detections Merge, Multi-Label Classification Model, Instance Segmentation Model, Bounding Box Visualization, Pixelate Visualization, Roboflow Custom Metadata, Trace Visualization, Detections Transformation, Segment Anything 2 Model, Webhook Sink, Dynamic Crop, Icon Visualization, Detections Classes Replacement, Instance Segmentation Model, Model Comparison Visualization, Single-Label Classification Model, Florence-2 Model, Single-Label Classification Model, SAM 3, Moondream2, Circle Visualization, Florence-2 Model, Ellipse Visualization, Object Detection Model, SmolVLM2, Crop Visualization, Color Visualization, Keypoint Visualization, Keypoint Detection Model, Label Visualization, SAM 3, Byte Tracker, Roboflow Dataset Upload, Detections Filter, Triangle Visualization, Background Color Visualization, Detection Offset, Keypoint Detection Model, Qwen2.5-VL, Multi-Label Classification Model, Object Detection Model, Corner 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: Line Counter, Detections Consensus, Llama 3.2 Vision, Blur Visualization, Dimension Collapse, Perspective Correction, Polygon Zone Visualization, Bounding Box Visualization, QR Code Generator, Pixelate Visualization, Distance Measurement, Trace Visualization, Roboflow Custom Metadata, Image Threshold, Polygon Visualization, Dynamic Crop, Icon Visualization, Image Slicer, Identify Outliers, Stability AI Outpainting, Model Comparison Visualization, Single-Label Classification Model, Dynamic Zone, Clip Comparison, OpenAI, Classification Label Visualization, Stitch Images, Size Measurement, Mask Visualization, Florence-2 Model, Relative Static Crop, Absolute Static Crop, SIFT Comparison, Google Gemini, Circle Visualization, Florence-2 Model, Ellipse Visualization, Image Convert Grayscale, Image Preprocessing, Color Visualization, Image Blur, Stability AI Image Generation, Anthropic Claude, Keypoint Visualization, Camera Calibration, Local File Sink, Keypoint Detection Model, VLM as Detector, Image Slicer, Line Counter, Email Notification, VLM as Detector, Roboflow Dataset Upload, Background Color Visualization, Triangle Visualization, Slack Notification, Multi-Label Classification Model, Object Detection Model, Halo Visualization, Corner Visualization, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Dot Visualization, Image Contours, Twilio SMS Notification, VLM as Classifier, Reference Path Visualization, Morphological Transformation, Motion Detection, OpenAI, Webhook Sink, PTZ Tracking (ONVIF).md), Instance Segmentation Model, Contrast Equalization, Camera Focus, Stability AI Inpainting, JSON Parser, Clip Comparison, Line Counter Visualization, Identify Changes, Template Matching, Email Notification, Crop Visualization, Grid Visualization, VLM as Classifier, Buffer, SIFT, Depth Estimation, Background Subtraction, Label Visualization, Anthropic Claude, Pixel Color Count, SIFT Comparison, OpenAI
• outputs: Line Counter, Path Deviation, Llama 3.2 Vision, Detections Consensus, Perception Encoder Embedding Model, Blur Visualization, SAM 3, Perspective Correction, Polygon Zone Visualization, Bounding Box Visualization, QR Code Generator, Distance Measurement, Pixelate Visualization, Trace Visualization, Roboflow Custom Metadata, Detections Transformation, Segment Anything 2 Model, Image Threshold, Polygon Visualization, Icon Visualization, Dynamic Crop, Stability AI Outpainting, Model Comparison Visualization, LMM, OpenAI, Cache Get, Classification Label Visualization, Size Measurement, Florence-2 Model, Mask Visualization, SAM 3, SIFT Comparison, Time in Zone, Moondream2, Google Gemini, Circle Visualization, Florence-2 Model, LMM For Classification, Time in Zone, Ellipse Visualization, Anthropic Claude, Image Preprocessing, Image Blur, Stability AI Image Generation, Color Visualization, Google Vision OCR, Keypoint Visualization, Local File Sink, Line Counter, Email Notification, Byte Tracker, Roboflow Dataset Upload, Detections Filter, Background Color Visualization, Triangle Visualization, Slack Notification, Halo Visualization, Corner Visualization, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Dot Visualization, Twilio SMS Notification, Detections Merge, Instance Segmentation Model, Seg Preview, Reference Path Visualization, Morphological Transformation, OpenAI, Webhook Sink, PTZ Tracking (ONVIF).md), Detections Classes Replacement, Instance Segmentation Model, Detections Stitch, Contrast Equalization, YOLO-World Model, Stitch OCR Detections, Stability AI Inpainting, Clip Comparison, CogVLM, Line Counter Visualization, Cache Set, Path Deviation, CLIP Embedding Model, Email Notification, Crop Visualization, OpenAI, SAM 3, Anthropic Claude, Label Visualization, Pixel Color Count, Time in Zone, Detection Offset, OpenAI

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"
}