Object Detection Model¶
v3¶
Class: RoboflowObjectDetectionModelBlockV3 (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 object-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_object_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 |
How confidence thresholds are determined.. | ✅ |
custom_confidence |
float |
Custom confidence threshold for predictions.. | ✅ |
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 Object Detection Model in version v3.
- inputs:
Keypoint Detection Model,Distance Measurement,Anthropic Claude,Instance Segmentation Model,SIFT Comparison,Google Vision OCR,Circle Visualization,Image Slicer,Google Gemini,Identify Outliers,Image Contours,Qwen 3.6 API,Single-Label Classification Model,Roboflow Vision Events,Depth Estimation,Line Counter Visualization,VLM As Detector,Stitch Images,Morphological Transformation,LMM,Model Comparison Visualization,Buffer,MoonshotAI Kimi,Grid Visualization,Instance Segmentation Model,Twilio SMS/MMS Notification,OpenAI,Clip Comparison,Twilio SMS Notification,Qwen-VL,S3 Sink,Halo Visualization,Camera Focus,SIFT Comparison,Semantic Segmentation Model,Multi-Label Classification Model,Keypoint Detection Model,Local File Sink,Mask Visualization,SIFT,Anthropic Claude,MoonshotAI Kimi,Roboflow Dataset Upload,Text Display,Image Slicer,Multi-Label Classification Model,Absolute Static Crop,Llama 3.2 Vision,VLM As Classifier,PTZ Tracking (ONVIF),GLM-OCR,Object Detection Model,Roboflow Custom Metadata,Email Notification,Dynamic Crop,Instance Segmentation Model,OpenRouter,Model Monitoring Inference Aggregator,Contrast Enhancement,Motion Detection,Webhook Sink,Google Gemma API,Stability AI Image Generation,Color Visualization,Heatmap Visualization,Contrast Equalization,Object Detection Model,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Slack Notification,Anthropic Claude,QR Code Generator,Clip Comparison,OpenAI,Bounding Box Visualization,Florence-2 Model,VLM As Classifier,Image Blur,Keypoint Detection Model,Detections Consensus,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,Image Threshold,LMM For Classification,Object Detection Model,Blur Visualization,Line Counter,Relative Static Crop,Dimension Collapse,Trace Visualization,Qwen3.5-VL,Size Measurement,Dynamic Zone,Florence-2 Model,Camera Focus,CogVLM,Pixelate Visualization,Llama 3.2 Vision,Image Convert Grayscale,Keypoint Visualization,Polygon Visualization,Line Counter,Google Gemma,Classification Label Visualization,Multi-Label Classification Model,Image Stack,Morphological Transformation,Camera Calibration,Google Gemini,Email Notification,Image Preprocessing,Semantic Segmentation Model,Corner Visualization,Stitch OCR Detections,Halo Visualization,Roboflow Asset Library Attributes,Reference Path Visualization,OpenAI,Background Color Visualization,Identify Changes,Single-Label Classification Model,JSON Parser,Ellipse Visualization,CSV Formatter,Stability AI Outpainting,VLM As Detector,EasyOCR,Triangle Visualization,OCR Model,Crop Visualization,Perspective Correction,Qwen 3.5 API,Detection Event Log,Stitch OCR Detections,OpenAI-Compatible LLM,Detections List Roll-Up,Instance Segmentation Model,Polygon Visualization,Background Subtraction,Template Matching,Pixel Color Count,OpenAI,Stability AI Inpainting - outputs:
Detections Stabilizer,Distance Measurement,Velocity,Detection Offset,Keypoint Detection Model,Instance Segmentation Model,Circle Visualization,Qwen3.5,Bounding Box Visualization,SAM 3,Florence-2 Model,Detections Filter,Overlap Filter,Keypoint Detection Model,Detections Consensus,Qwen2.5-VL,Detections Merge,Dot Visualization,Single-Label Classification Model,Label Visualization,Icon Visualization,Byte Tracker,Roboflow Vision Events,Object Detection Model,OC-SORT Tracker,Blur Visualization,Line Counter,Model Comparison Visualization,Size Measurement,Trace Visualization,Qwen3.5-VL,Moondream2,Segment Anything 2 Model,Florence-2 Model,Pixelate Visualization,Instance Segmentation Model,Time in Zone,Detections Classes Replacement,Byte Tracker,SAM 3,Qwen-VL,SAM 3,Line Counter,Camera Focus,Keypoint Detection Model,Semantic Segmentation Model,Multi-Label Classification Model,Multi-Label Classification Model,SmolVLM2,Path Deviation,Roboflow Dataset Upload,Semantic Segmentation Model,Corner Visualization,Multi-Label Classification Model,Stitch OCR Detections,Detections Transformation,Byte Tracker,PTZ Tracking (ONVIF),Path Deviation,GLM-OCR,ByteTrack Tracker,Roboflow Custom Metadata,Detections Combine,Mask Area Measurement,Dynamic Crop,Object Detection Model,Instance Segmentation Model,Overlap Analysis,Time in Zone,Background Color Visualization,Detections Stitch,Single-Label Classification Model,Model Monitoring Inference Aggregator,Ellipse Visualization,Triangle Visualization,Qwen3-VL,Crop Visualization,Per-Class Confidence Filter,Webhook Sink,Perspective Correction,SAM2 Video Tracker,Detection Event Log,Stitch OCR Detections,Color Visualization,Heatmap Visualization,Detections List Roll-Up,BoT-SORT Tracker,Instance Segmentation Model,Object Detection Model,SORT Tracker,Time in Zone,Roboflow Dataset Upload,Single-Label Classification Model
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Object 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): How confidence thresholds are determined..custom_confidence(float_zero_to_one): Custom confidence threshold for predictions..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(object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Object Detection Model in version v3
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_object_detection_model@v3",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence_mode": "<block_does_not_provide_example>",
"custom_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: RoboflowObjectDetectionModelBlockV2 (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 object-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_object_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.. | ✅ |
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 Object Detection Model in version v2.
- inputs:
Keypoint Detection Model,Distance Measurement,Anthropic Claude,SIFT Comparison,Circle Visualization,Image Slicer,Google Gemini,Identify Outliers,Image Contours,Qwen 3.6 API,Roboflow Vision Events,Depth Estimation,Line Counter Visualization,VLM As Detector,Stitch Images,Morphological Transformation,Model Comparison Visualization,Buffer,MoonshotAI Kimi,Grid Visualization,Instance Segmentation Model,Twilio SMS/MMS Notification,OpenAI,Clip Comparison,Twilio SMS Notification,Qwen-VL,S3 Sink,Local File Sink,Halo Visualization,Camera Focus,SIFT Comparison,Semantic Segmentation Model,Multi-Label Classification Model,Mask Visualization,SIFT,Anthropic Claude,MoonshotAI Kimi,Roboflow Dataset Upload,Text Display,Image Slicer,Absolute Static Crop,Llama 3.2 Vision,VLM As Classifier,PTZ Tracking (ONVIF),Roboflow Custom Metadata,Email Notification,Dynamic Crop,Instance Segmentation Model,OpenRouter,Model Monitoring Inference Aggregator,Contrast Enhancement,Motion Detection,Webhook Sink,Google Gemma API,Stability AI Image Generation,Color Visualization,Heatmap Visualization,Contrast Equalization,Object Detection Model,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Slack Notification,Anthropic Claude,QR Code Generator,Clip Comparison,OpenAI,Bounding Box Visualization,Florence-2 Model,VLM As Classifier,Image Blur,Keypoint Detection Model,Detections Consensus,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,Image Threshold,Object Detection Model,Blur Visualization,Line Counter,Relative Static Crop,Dimension Collapse,Trace Visualization,Size Measurement,Dynamic Zone,Florence-2 Model,Camera Focus,Pixelate Visualization,Llama 3.2 Vision,Image Convert Grayscale,Keypoint Visualization,Polygon Visualization,Line Counter,Google Gemma,Classification Label Visualization,Multi-Label Classification Model,Image Stack,Morphological Transformation,Camera Calibration,Google Gemini,Email Notification,Image Preprocessing,Semantic Segmentation Model,Corner Visualization,Halo Visualization,Roboflow Asset Library Attributes,Reference Path Visualization,Background Color Visualization,Identify Changes,Single-Label Classification Model,JSON Parser,Ellipse Visualization,Stability AI Outpainting,VLM As Detector,Triangle Visualization,Crop Visualization,Perspective Correction,Qwen 3.5 API,Detection Event Log,Detections List Roll-Up,Instance Segmentation Model,Polygon Visualization,Background Subtraction,Template Matching,Pixel Color Count,OpenAI,Stability AI Inpainting - outputs:
Detections Stabilizer,Distance Measurement,Velocity,Detection Offset,Keypoint Detection Model,Instance Segmentation Model,Circle Visualization,Qwen3.5,Bounding Box Visualization,SAM 3,Florence-2 Model,Detections Filter,Overlap Filter,Keypoint Detection Model,Detections Consensus,Qwen2.5-VL,Detections Merge,Dot Visualization,Single-Label Classification Model,Label Visualization,Icon Visualization,Byte Tracker,Roboflow Vision Events,Object Detection Model,OC-SORT Tracker,Blur Visualization,Line Counter,Model Comparison Visualization,Size Measurement,Trace Visualization,Qwen3.5-VL,Moondream2,Segment Anything 2 Model,Florence-2 Model,Pixelate Visualization,Instance Segmentation Model,Time in Zone,Detections Classes Replacement,Byte Tracker,SAM 3,Qwen-VL,SAM 3,Line Counter,Camera Focus,Keypoint Detection Model,Semantic Segmentation Model,Multi-Label Classification Model,Multi-Label Classification Model,SmolVLM2,Path Deviation,Roboflow Dataset Upload,Semantic Segmentation Model,Corner Visualization,Multi-Label Classification Model,Stitch OCR Detections,Detections Transformation,Byte Tracker,PTZ Tracking (ONVIF),Path Deviation,GLM-OCR,ByteTrack Tracker,Roboflow Custom Metadata,Detections Combine,Mask Area Measurement,Dynamic Crop,Object Detection Model,Instance Segmentation Model,Overlap Analysis,Time in Zone,Background Color Visualization,Detections Stitch,Single-Label Classification Model,Model Monitoring Inference Aggregator,Ellipse Visualization,Triangle Visualization,Qwen3-VL,Crop Visualization,Per-Class Confidence Filter,Webhook Sink,Perspective Correction,SAM2 Video Tracker,Detection Event Log,Stitch OCR Detections,Color Visualization,Heatmap Visualization,Detections List Roll-Up,BoT-SORT Tracker,Instance Segmentation Model,Object Detection Model,SORT Tracker,Time in Zone,Roboflow Dataset Upload,Single-Label Classification Model
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Object 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..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(object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Object Detection Model in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_object_detection_model@v2",
"images": "$inputs.image",
"model_id": "my_project/3",
"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: RoboflowObjectDetectionModelBlockV1 (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 object-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_object_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.. | ✅ |
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 Object Detection Model in version v1.
- inputs:
Keypoint Detection Model,Distance Measurement,Anthropic Claude,SIFT Comparison,Circle Visualization,Image Slicer,Google Gemini,Identify Outliers,Image Contours,Qwen 3.6 API,Roboflow Vision Events,Depth Estimation,Line Counter Visualization,VLM As Detector,Stitch Images,Morphological Transformation,Model Comparison Visualization,Buffer,MoonshotAI Kimi,Grid Visualization,Instance Segmentation Model,Twilio SMS/MMS Notification,OpenAI,Clip Comparison,Twilio SMS Notification,Qwen-VL,S3 Sink,Local File Sink,Halo Visualization,Camera Focus,SIFT Comparison,Semantic Segmentation Model,Multi-Label Classification Model,Mask Visualization,SIFT,Anthropic Claude,MoonshotAI Kimi,Roboflow Dataset Upload,Text Display,Image Slicer,Absolute Static Crop,Llama 3.2 Vision,VLM As Classifier,PTZ Tracking (ONVIF),Roboflow Custom Metadata,Email Notification,Dynamic Crop,Instance Segmentation Model,OpenRouter,Model Monitoring Inference Aggregator,Contrast Enhancement,Motion Detection,Webhook Sink,Google Gemma API,Stability AI Image Generation,Color Visualization,Heatmap Visualization,Contrast Equalization,Object Detection Model,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Slack Notification,Anthropic Claude,QR Code Generator,Clip Comparison,OpenAI,Bounding Box Visualization,Florence-2 Model,VLM As Classifier,Image Blur,Keypoint Detection Model,Detections Consensus,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,Image Threshold,Object Detection Model,Blur Visualization,Line Counter,Relative Static Crop,Dimension Collapse,Trace Visualization,Size Measurement,Dynamic Zone,Florence-2 Model,Camera Focus,Pixelate Visualization,Llama 3.2 Vision,Image Convert Grayscale,Keypoint Visualization,Polygon Visualization,Line Counter,Google Gemma,Classification Label Visualization,Multi-Label Classification Model,Image Stack,Morphological Transformation,Camera Calibration,Google Gemini,Email Notification,Image Preprocessing,Semantic Segmentation Model,Corner Visualization,Halo Visualization,Roboflow Asset Library Attributes,Reference Path Visualization,Background Color Visualization,Identify Changes,Single-Label Classification Model,JSON Parser,Ellipse Visualization,Stability AI Outpainting,VLM As Detector,Triangle Visualization,Crop Visualization,Perspective Correction,Qwen 3.5 API,Detection Event Log,Detections List Roll-Up,Instance Segmentation Model,Polygon Visualization,Background Subtraction,Template Matching,Pixel Color Count,OpenAI,Stability AI Inpainting - outputs:
Detections Stabilizer,Distance Measurement,Velocity,Instance Segmentation Model,Anthropic Claude,Google Vision OCR,Circle Visualization,Detections Filter,Google Gemini,Qwen 3.6 API,Detections Merge,CLIP Embedding Model,Byte Tracker,Roboflow Vision Events,Depth Estimation,Line Counter Visualization,Morphological Transformation,LMM,Model Comparison Visualization,MoonshotAI Kimi,Segment Anything 2 Model,Cache Set,Instance Segmentation Model,Twilio SMS/MMS Notification,OpenAI,Detections Classes Replacement,Twilio SMS Notification,SAM 3,Qwen-VL,S3 Sink,SAM 3,Halo Visualization,Local File Sink,Semantic Segmentation Model,SIFT Comparison,Multi-Label Classification Model,Camera Focus,Mask Visualization,Path Deviation,Anthropic Claude,MoonshotAI Kimi,Roboflow Dataset Upload,Text Display,Llama 3.2 Vision,PTZ Tracking (ONVIF),Path Deviation,GLM-OCR,Email Notification,Seg Preview,Dynamic Crop,Roboflow Custom Metadata,Detections Combine,Instance Segmentation Model,Mask Area Measurement,Overlap Analysis,Time in Zone,OpenRouter,Model Monitoring Inference Aggregator,Per-Class Confidence Filter,Webhook Sink,Google Gemma API,Stability AI Image Generation,SAM2 Video Tracker,Color Visualization,Heatmap Visualization,Contrast Equalization,YOLO-World Model,Google Gemini,Roboflow Dataset Upload,Slack Notification,Anthropic Claude,QR Code Generator,Detection Offset,Clip Comparison,OpenAI,Bounding Box Visualization,SAM 3,Florence-2 Model,Overlap Filter,Image Blur,Keypoint Detection Model,Detections Consensus,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,Image Threshold,Cache Get,LMM For Classification,Object Detection Model,OC-SORT Tracker,Line Counter,Blur Visualization,Trace Visualization,Moondream2,Size Measurement,Perception Encoder Embedding Model,Florence-2 Model,CogVLM,Pixelate Visualization,Llama 3.2 Vision,Time in Zone,Byte Tracker,Keypoint Visualization,Polygon Visualization,Line Counter,Google Gemma,Classification Label Visualization,Morphological Transformation,Email Notification,Google Gemini,Image Preprocessing,Corner Visualization,Stitch OCR Detections,Detections Transformation,Halo Visualization,Byte Tracker,Roboflow Asset Library Attributes,ByteTrack Tracker,OpenAI,Reference Path Visualization,Detections Stitch,Background Color Visualization,Single-Label Classification Model,Ellipse Visualization,Stability AI Outpainting,Triangle Visualization,Crop Visualization,Qwen 3.5 API,Perspective Correction,Detection Event Log,Stitch OCR Detections,OpenAI-Compatible LLM,Detections List Roll-Up,BoT-SORT Tracker,Instance Segmentation Model,SORT Tracker,Time in Zone,Polygon Visualization,Pixel Color Count,OpenAI,Stability AI Inpainting
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Object 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..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(object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
Example JSON definition of step Object Detection Model in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_object_detection_model@v1",
"images": "$inputs.image",
"model_id": "my_project/3",
"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"
}