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

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"
}