Keypoint Detection Model¶
v3¶
Class: RoboflowKeypointDetectionModelBlockV3 (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@v3to 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_mode |
str |
not available. | ✅ |
custom_confidence |
float |
not available. | ✅ |
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 v3.
- inputs:
Keypoint Visualization,Keypoint Detection Model,Twilio SMS/MMS Notification,OPC UA Writer Sink,Qwen 3.6 API,SIFT Comparison,Grid Visualization,Object Detection Model,Llama 3.2 Vision,PLC Writer,JSON Parser,Absolute Static Crop,Distance Measurement,Roboflow Visual Search Classifier,Image Threshold,Polygon Zone Visualization,CogVLM,MQTT Writer,Contrast Equalization,Google Gemini,Detections List Roll-Up,OCR Model,GLM-OCR,Background Subtraction,Contrast Enhancement,Reference Path Visualization,Twilio SMS Notification,Instance Segmentation Model,Google Gemma API,PTZ Tracking (ONVIF),VLM As Detector,Florence-2 Model,Dynamic Crop,Halo Visualization,Image Blur,LMM,Ellipse Visualization,OpenAI,Florence-2 Model,Line Counter,LMM For Classification,Semantic Segmentation Model,Instance Segmentation Model,Event Writer,OpenAI,Single-Label Classification Model,Keypoint Detection Model,Google Gemma,Buffer,Slack Notification,Heatmap Visualization,Semantic Segmentation Model,Email Notification,Circle Visualization,Perspective Correction,Camera Focus,Crop Visualization,Polygon Visualization,Keypoint Detection Model,Detections Consensus,Line Counter,Multi-Label Classification Model,Stability AI Inpainting,OpenAI-Compatible LLM,Object Detection Model,VLM As Classifier,PLC ModbusTCP,Image Slicer,Google Gemini,Roboflow Vision Events,Multi-Label Classification Model,Size Measurement,Local File Sink,Object Detection Model,Dot Visualization,Line Counter Visualization,Instance Segmentation Model,Depth Estimation,Stitch OCR Detections,PP-OCR,Blur Visualization,EasyOCR,Clip Comparison,Anthropic Claude,PLC EthernetIP,CSV Formatter,Color Visualization,Dynamic Zone,Image Convert Grayscale,Motion Detection,Stitch OCR Detections,Single-Label Classification Model,Qwen 3.5 API,Trace Visualization,Icon Visualization,Current Time,PLC Reader,Model Comparison Visualization,Camera Focus,QR Code Generator,Google Gemini,Image Stack,Instance Segmentation Model,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Microsoft SQL Server Sink,Text Display,Stability AI Image Generation,Google Vision OCR,Webhook Sink,OpenAI,VLM As Detector,Identify Outliers,Pixelate Visualization,Stitch Images,S3 Sink,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Anthropic Claude,Dimension Collapse,Roboflow Dataset Upload,Morphological Transformation,Detection Event Log,Bounding Box Visualization,Template Matching,VLM As Classifier,Image Contours,Image Slicer,Stability AI Outpainting,Anthropic Claude,Roboflow Custom Metadata,Multi-Label Classification Model,Camera Calibration,Roboflow Asset Library Attributes,Morphological Transformation,Relative Static Crop,Background Color Visualization,Model Monitoring Inference Aggregator,Clip Comparison,Corner Visualization,GeoTag Detection,SIFT,Roboflow Visual Search,OpenRouter,Llama 3.2 Vision,Qwen3.5-VL,Identify Changes,Email Notification,Single-Label Classification Model,Triangle Visualization,OpenAI,Roboflow Dataset Upload,Pixel Color Count,Image Preprocessing,SIFT Comparison,Halo Visualization,Label Visualization - outputs:
Keypoint Visualization,Keypoint Detection Model,Blur Visualization,Segment Anything 2 Model,Object Detection Model,SAM 3,Color Visualization,Per-Class Confidence Filter,Qwen2.5-VL,Qwen3-VL,Single-Label Classification Model,Velocity,Trace Visualization,Icon Visualization,Model Comparison Visualization,BoT-SORT Tracker,Detections List Roll-Up,GLM-OCR,Instance Segmentation Model,Qwen-VL,Instance Segmentation Model,Detections Classes Replacement,Detections Merge,Detections Filter,Florence-2 Model,Dynamic Crop,SAM 3 Interactive,SAM 3,Webhook Sink,Ellipse Visualization,Pixelate Visualization,Florence-2 Model,Detection Offset,Semantic Segmentation Model,Roboflow Dataset Upload,Event Writer,Instance Segmentation Model,Single-Label Classification Model,Keypoint Detection Model,Bounding Box Visualization,SmolVLM2,Heatmap Visualization,SORT Tracker,Semantic Segmentation Model,Circle Visualization,Roboflow Custom Metadata,Multi-Label Classification Model,SAM2 Video Tracker,Byte Tracker,Detections Consensus,Crop Visualization,Keypoint Detection Model,ByteTrack Tracker,Multi-Label Classification Model,Background Color Visualization,Model Monitoring Inference Aggregator,Corner Visualization,SAM 3,Object Detection Model,Track Class Lock,Roboflow Vision Events,Moondream2,Detections Transformation,Multi-Label Classification Model,Qwen3.5-VL,Qwen3.5,Object Detection Model,Single-Label Classification Model,SAM3 Video Tracker,Triangle Visualization,Roboflow Dataset Upload,OC-SORT Tracker,Dot Visualization,Instance Segmentation Model,Label Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Keypoint Detection Model in version v3 has.
Bindings
-
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): not available.custom_confidence(float_zero_to_one): not available.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 v3
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_keypoint_detection_model@v3",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence_mode": "<block_does_not_provide_example>",
"custom_confidence": "<block_does_not_provide_example>",
"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"
}
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:
Keypoint Visualization,Keypoint Detection Model,Twilio SMS/MMS Notification,OPC UA Writer Sink,Qwen 3.6 API,SIFT Comparison,Grid Visualization,Object Detection Model,Llama 3.2 Vision,PLC Writer,JSON Parser,Absolute Static Crop,Distance Measurement,Roboflow Visual Search Classifier,Image Threshold,Polygon Zone Visualization,MQTT Writer,Contrast Equalization,Google Gemini,Detections List Roll-Up,Background Subtraction,Contrast Enhancement,Reference Path Visualization,Google Gemma API,Twilio SMS Notification,PTZ Tracking (ONVIF),VLM As Detector,Florence-2 Model,Dynamic Crop,Halo Visualization,Image Blur,Ellipse Visualization,OpenAI,Florence-2 Model,Line Counter,Semantic Segmentation Model,Instance Segmentation Model,OpenAI,Event Writer,Single-Label Classification Model,Google Gemma,Buffer,Slack Notification,Heatmap Visualization,Semantic Segmentation Model,Email Notification,Circle Visualization,Perspective Correction,Camera Focus,Crop Visualization,Polygon Visualization,Keypoint Detection Model,Detections Consensus,Line Counter,Stability AI Inpainting,VLM As Classifier,PLC ModbusTCP,Image Slicer,Google Gemini,Roboflow Vision Events,Multi-Label Classification Model,Size Measurement,Local File Sink,Object Detection Model,Dot Visualization,Line Counter Visualization,Depth Estimation,Instance Segmentation Model,Blur Visualization,Clip Comparison,Anthropic Claude,PLC EthernetIP,Color Visualization,Dynamic Zone,Image Convert Grayscale,Motion Detection,Qwen 3.5 API,Trace Visualization,Icon Visualization,PLC Reader,Model Comparison Visualization,Camera Focus,QR Code Generator,Google Gemini,Image Stack,Instance Segmentation Model,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Microsoft SQL Server Sink,Text Display,Stability AI Image Generation,Webhook Sink,OpenAI,VLM As Detector,Identify Outliers,Pixelate Visualization,Stitch Images,S3 Sink,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Dimension Collapse,Anthropic Claude,Roboflow Dataset Upload,Morphological Transformation,Detection Event Log,Bounding Box Visualization,Template Matching,VLM As Classifier,Image Contours,Image Slicer,Stability AI Outpainting,Anthropic Claude,Roboflow Custom Metadata,Multi-Label Classification Model,Camera Calibration,Roboflow Asset Library Attributes,Morphological Transformation,Relative Static Crop,Background Color Visualization,Model Monitoring Inference Aggregator,Clip Comparison,Corner Visualization,GeoTag Detection,SIFT,Roboflow Visual Search,OpenRouter,Identify Changes,Llama 3.2 Vision,Email Notification,Single-Label Classification Model,Triangle Visualization,Pixel Color Count,Roboflow Dataset Upload,Image Preprocessing,SIFT Comparison,Halo Visualization,Label Visualization - outputs:
Keypoint Visualization,Keypoint Detection Model,Blur Visualization,Segment Anything 2 Model,Object Detection Model,SAM 3,Color Visualization,Per-Class Confidence Filter,Qwen2.5-VL,Qwen3-VL,Single-Label Classification Model,Velocity,Trace Visualization,Icon Visualization,Model Comparison Visualization,BoT-SORT Tracker,Detections List Roll-Up,GLM-OCR,Instance Segmentation Model,Qwen-VL,Instance Segmentation Model,Detections Classes Replacement,Detections Merge,Detections Filter,Florence-2 Model,Dynamic Crop,SAM 3 Interactive,SAM 3,Webhook Sink,Ellipse Visualization,Pixelate Visualization,Florence-2 Model,Detection Offset,Semantic Segmentation Model,Roboflow Dataset Upload,Event Writer,Instance Segmentation Model,Single-Label Classification Model,Keypoint Detection Model,Bounding Box Visualization,SmolVLM2,Heatmap Visualization,SORT Tracker,Semantic Segmentation Model,Circle Visualization,Roboflow Custom Metadata,Multi-Label Classification Model,SAM2 Video Tracker,Byte Tracker,Detections Consensus,Crop Visualization,Keypoint Detection Model,ByteTrack Tracker,Multi-Label Classification Model,Background Color Visualization,Model Monitoring Inference Aggregator,Corner Visualization,SAM 3,Object Detection Model,Track Class Lock,Roboflow Vision Events,Moondream2,Detections Transformation,Multi-Label Classification Model,Qwen3.5-VL,Qwen3.5,Object Detection Model,Single-Label Classification Model,SAM3 Video Tracker,Triangle Visualization,Roboflow Dataset Upload,OC-SORT Tracker,Dot Visualization,Instance Segmentation Model,Label 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:
Keypoint Visualization,Keypoint Detection Model,Twilio SMS/MMS Notification,OPC UA Writer Sink,Qwen 3.6 API,SIFT Comparison,Grid Visualization,Object Detection Model,Llama 3.2 Vision,PLC Writer,JSON Parser,Absolute Static Crop,Distance Measurement,Roboflow Visual Search Classifier,Image Threshold,Polygon Zone Visualization,MQTT Writer,Contrast Equalization,Google Gemini,Detections List Roll-Up,Background Subtraction,Contrast Enhancement,Reference Path Visualization,Google Gemma API,Twilio SMS Notification,PTZ Tracking (ONVIF),VLM As Detector,Florence-2 Model,Dynamic Crop,Halo Visualization,Image Blur,Ellipse Visualization,OpenAI,Florence-2 Model,Line Counter,Semantic Segmentation Model,Instance Segmentation Model,OpenAI,Event Writer,Single-Label Classification Model,Google Gemma,Buffer,Slack Notification,Heatmap Visualization,Semantic Segmentation Model,Email Notification,Circle Visualization,Perspective Correction,Camera Focus,Crop Visualization,Polygon Visualization,Keypoint Detection Model,Detections Consensus,Line Counter,Stability AI Inpainting,VLM As Classifier,PLC ModbusTCP,Image Slicer,Google Gemini,Roboflow Vision Events,Multi-Label Classification Model,Size Measurement,Local File Sink,Object Detection Model,Dot Visualization,Line Counter Visualization,Depth Estimation,Instance Segmentation Model,Blur Visualization,Clip Comparison,Anthropic Claude,PLC EthernetIP,Color Visualization,Dynamic Zone,Image Convert Grayscale,Motion Detection,Qwen 3.5 API,Trace Visualization,Icon Visualization,PLC Reader,Model Comparison Visualization,Camera Focus,QR Code Generator,Google Gemini,Image Stack,Instance Segmentation Model,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Microsoft SQL Server Sink,Text Display,Stability AI Image Generation,Webhook Sink,OpenAI,VLM As Detector,Identify Outliers,Pixelate Visualization,Stitch Images,S3 Sink,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Dimension Collapse,Anthropic Claude,Roboflow Dataset Upload,Morphological Transformation,Detection Event Log,Bounding Box Visualization,Template Matching,VLM As Classifier,Image Contours,Image Slicer,Stability AI Outpainting,Anthropic Claude,Roboflow Custom Metadata,Multi-Label Classification Model,Camera Calibration,Roboflow Asset Library Attributes,Morphological Transformation,Relative Static Crop,Background Color Visualization,Model Monitoring Inference Aggregator,Clip Comparison,Corner Visualization,GeoTag Detection,SIFT,Roboflow Visual Search,OpenRouter,Identify Changes,Llama 3.2 Vision,Email Notification,Single-Label Classification Model,Triangle Visualization,Pixel Color Count,Roboflow Dataset Upload,Image Preprocessing,SIFT Comparison,Halo Visualization,Label Visualization - outputs:
Keypoint Visualization,Twilio SMS/MMS Notification,OPC UA Writer Sink,Keypoint Detection Model,Perception Encoder Embedding Model,Qwen 3.6 API,SAM 3,Llama 3.2 Vision,Distance Measurement,Roboflow Visual Search Classifier,Velocity,Image Threshold,Polygon Zone Visualization,CogVLM,BoT-SORT Tracker,MQTT Writer,Contrast Equalization,Google Gemini,Detections List Roll-Up,GLM-OCR,Reference Path Visualization,Twilio SMS Notification,Google Gemma API,Instance Segmentation Model,PTZ Tracking (ONVIF),Detections Classes Replacement,Detections Merge,Detections Filter,Florence-2 Model,Dynamic Crop,Halo Visualization,Image Blur,LMM,SAM 3 Interactive,Seg Preview,SAM 3,Ellipse Visualization,OpenAI,Florence-2 Model,Line Counter,LMM For Classification,Time in Zone,Cache Set,Semantic Segmentation Model,OpenAI,Path Deviation,Event Writer,Instance Segmentation Model,Time in Zone,Google Gemma,Slack Notification,Heatmap Visualization,SORT Tracker,Email Notification,Circle Visualization,Perspective Correction,Byte Tracker,Crop Visualization,Polygon Visualization,Detections Consensus,Line Counter,ByteTrack Tracker,Stability AI Inpainting,OpenAI-Compatible LLM,SAM 3,Google Gemini,Roboflow Vision Events,Detections Transformation,Multi-Label Classification Model,Size Measurement,Local File Sink,Object Detection Model,SAM3 Video Tracker,Dot Visualization,Line Counter Visualization,Time in Zone,Instance Segmentation Model,Depth Estimation,Stitch OCR Detections,Blur Visualization,Anthropic Claude,Segment Anything 2 Model,Color Visualization,Per-Class Confidence Filter,Stitch OCR Detections,Qwen 3.5 API,Trace Visualization,Icon Visualization,Current Time,Cache Get,Model Comparison Visualization,QR Code Generator,Google Gemini,Detections Stitch,Instance Segmentation Model,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Microsoft SQL Server Sink,Text Display,Stability AI Image Generation,Google Vision OCR,Webhook Sink,OpenAI,Pixelate Visualization,Detection Offset,S3 Sink,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Anthropic Claude,Roboflow Dataset Upload,Morphological Transformation,Bounding Box Visualization,Stability AI Outpainting,Anthropic Claude,Roboflow Custom Metadata,SAM2 Video Tracker,Roboflow Asset Library Attributes,Morphological Transformation,Path Deviation,Background Color Visualization,Model Monitoring Inference Aggregator,Clip Comparison,Corner Visualization,Track Class Lock,Moondream2,Roboflow Visual Search,OpenRouter,Qwen3.5-VL,Llama 3.2 Vision,Email Notification,CLIP Embedding Model,Single-Label Classification Model,Triangle Visualization,OpenAI,Pixel Color Count,Roboflow Dataset Upload,OC-SORT Tracker,Image Preprocessing,SIFT Comparison,YOLO-World Model,Halo Visualization,Label 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"
}