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

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"
}