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

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"
}