Keypoint Detection Model¶
v2¶
Class: RoboflowKeypointDetectionModelBlockV2 (there are multiple versions of this block)
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:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,VLM as Detector,VLM as Classifier,Polygon Zone Visualization,Camera Focus,Image Slicer,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Stability AI Inpainting,Pixelate Visualization,Line Counter,Detections Consensus,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Grid Visualization,VLM as Classifier,Dynamic Zone,Instance Segmentation Model,Camera Calibration,Email Notification,Object Detection Model,Classification Label Visualization,Llama 3.2 Vision,Roboflow Dataset Upload,Ellipse Visualization,Pixel Color Count,Size Measurement,Bounding Box Visualization,JSON Parser,Line Counter Visualization,Image Preprocessing,Keypoint Detection Model,Trace Visualization,Label Visualization,Clip Comparison,Local File Sink,Image Slicer,Anthropic Claude,Crop Visualization,Identify Outliers,Relative Static Crop,Model Comparison Visualization,Perspective Correction,OpenAI,Mask Visualization,Clip Comparison,Dynamic Crop,Template Matching,Florence-2 Model,Image Contours,Buffer,SIFT,SIFT Comparison,Reference Path Visualization,Multi-Label Classification Model,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,VLM as Detector,Keypoint Visualization,Identify Changes,Roboflow Custom Metadata,Line Counter,Stitch Images,Dimension Collapse,Blur Visualization - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Object Detection Model,Keypoint Detection Model,Trace Visualization,Label Visualization,Detections Transformation,Crop Visualization,Dot Visualization,Detections Merge,Detection Offset,Model Comparison Visualization,Roboflow Dataset Upload,Single-Label Classification Model,Pixelate Visualization,Detections Consensus,Qwen2.5-VL,Detections Filter,Webhook Sink,Color Visualization,Dynamic Crop,Florence-2 Model,Detections Classes Replacement,Instance Segmentation Model,Keypoint Detection Model,Instance Segmentation Model,Florence-2 Model,Object Detection Model,Multi-Label Classification Model,Triangle Visualization,Model Monitoring Inference Aggregator,Single-Label Classification Model,Roboflow Dataset Upload,Keypoint Visualization,Multi-Label Classification Model,Roboflow Custom Metadata,Segment Anything 2 Model,Ellipse Visualization,Blur 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)
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:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,VLM as Detector,VLM as Classifier,Polygon Zone Visualization,Camera Focus,Image Slicer,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Stability AI Inpainting,Pixelate Visualization,Line Counter,Detections Consensus,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Grid Visualization,VLM as Classifier,Dynamic Zone,Instance Segmentation Model,Camera Calibration,Email Notification,Object Detection Model,Classification Label Visualization,Llama 3.2 Vision,Roboflow Dataset Upload,Ellipse Visualization,Pixel Color Count,Size Measurement,Bounding Box Visualization,JSON Parser,Line Counter Visualization,Image Preprocessing,Keypoint Detection Model,Trace Visualization,Label Visualization,Clip Comparison,Local File Sink,Image Slicer,Anthropic Claude,Crop Visualization,Identify Outliers,Relative Static Crop,Model Comparison Visualization,Perspective Correction,OpenAI,Mask Visualization,Clip Comparison,Dynamic Crop,Template Matching,Florence-2 Model,Image Contours,Buffer,SIFT,SIFT Comparison,Reference Path Visualization,Multi-Label Classification Model,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,VLM as Detector,Keypoint Visualization,Identify Changes,Roboflow Custom Metadata,Line Counter,Stitch Images,Dimension Collapse,Blur Visualization - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,LMM,Polygon Zone Visualization,Image Blur,Cache Set,Dot Visualization,Path Deviation,Google Gemini,Detections Merge,Detection Offset,Roboflow Dataset Upload,Stability AI Inpainting,Pixelate Visualization,Line Counter,OpenAI,Detections Consensus,Distance Measurement,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Detections Classes Replacement,CogVLM,Instance Segmentation Model,Email Notification,Classification Label Visualization,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Ellipse Visualization,Size Measurement,Pixel Color Count,Cache Get,Bounding Box Visualization,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Local File Sink,Detections Transformation,Anthropic Claude,Crop Visualization,Detections Stitch,YOLO-World Model,Model Comparison Visualization,Perspective Correction,OpenAI,Path Deviation,Mask Visualization,Time in Zone,Detections Filter,Clip Comparison,Time in Zone,Dynamic Crop,Florence-2 Model,Instance Segmentation Model,Florence-2 Model,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,Keypoint Visualization,LMM For Classification,Roboflow Custom Metadata,Line Counter,Segment Anything 2 Model,Blur 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"
}