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:
S3 Sink,Keypoint Detection Model,Email Notification,Morphological Transformation,Clip Comparison,VLM As Detector,Qwen-VL,Twilio SMS/MMS Notification,Line Counter,MoonshotAI Kimi,Polygon Zone Visualization,Stitch OCR Detections,OpenAI-Compatible LLM,OpenAI,VLM As Detector,Heatmap Visualization,Keypoint Visualization,Email Notification,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Camera Focus,Label Visualization,Instance Segmentation Model,Local File Sink,Multi-Label Classification Model,Google Gemini,Motion Detection,Background Color Visualization,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,SIFT Comparison,Grid Visualization,Florence-2 Model,Detection Event Log,Single-Label Classification Model,OCR Model,VLM As Classifier,LMM For Classification,Keypoint Detection Model,Image Preprocessing,SIFT,Roboflow Dataset Upload,Dynamic Zone,Corner Visualization,Stability AI Outpainting,Halo Visualization,Multi-Label Classification Model,Qwen3.5-VL,Semantic Segmentation Model,Blur Visualization,Detections List Roll-Up,Distance Measurement,Morphological Transformation,Trace Visualization,VLM As Classifier,Stitch OCR Detections,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,Pixel Color Count,JSON Parser,Background Subtraction,Text Display,Absolute Static Crop,CSV Formatter,Florence-2 Model,Identify Outliers,Icon Visualization,Object Detection Model,Perspective Correction,Stability AI Inpainting,Image Convert Grayscale,Object Detection Model,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,Anthropic Claude,Dynamic Crop,Detections Consensus,Size Measurement,Clip Comparison,Contrast Enhancement,Bounding Box Visualization,Depth Estimation,Keypoint Detection Model,Image Contours,EasyOCR,Relative Static Crop,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Template Matching,Single-Label Classification Model,Image Blur,Anthropic Claude,Triangle Visualization,Object Detection Model,Roboflow Custom Metadata,OpenAI,SIFT Comparison,Slack Notification,Image Stack,Pixelate Visualization,Stitch Images,Single-Label Classification Model,Instance Segmentation Model,OpenAI,Buffer,Image Slicer,Line Counter Visualization,Image Slicer,Semantic Segmentation Model,LMM,Roboflow Dataset Upload,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Ellipse Visualization,PTZ Tracking (ONVIF),Identify Changes,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,Dimension Collapse,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Keypoint Detection Model,Path Deviation,SAM 3,Object Detection Model,Line Counter,Qwen-VL,Model Monitoring Inference Aggregator,Line Counter,Time in Zone,Stitch OCR Detections,OC-SORT Tracker,Dynamic Crop,Size Measurement,Detections Consensus,Heatmap Visualization,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Qwen3.5,Bounding Box Visualization,Overlap Filter,Detection Offset,Multi-Label Classification Model,SmolVLM2,Keypoint Detection Model,Multi-Label Classification Model,Byte Tracker,Background Color Visualization,Single-Label Classification Model,Instance Segmentation Model,Moondream2,Velocity,Per-Class Confidence Filter,Detection Event Log,Florence-2 Model,Triangle Visualization,Time in Zone,Single-Label Classification Model,Roboflow Custom Metadata,Object Detection Model,Detections Filter,Qwen2.5-VL,Detections Merge,Pixelate Visualization,Single-Label Classification Model,Instance Segmentation Model,Detections Stabilizer,Keypoint Detection Model,Roboflow Dataset Upload,Detections Classes Replacement,Semantic Segmentation Model,Segment Anything 2 Model,Corner Visualization,Multi-Label Classification Model,Roboflow Dataset Upload,Qwen3-VL,Detections Transformation,Time in Zone,Color Visualization,Blur Visualization,Detections List Roll-Up,Qwen3.5-VL,Semantic Segmentation Model,Camera Focus,Distance Measurement,Trace Visualization,Detections Stitch,Stitch OCR Detections,Byte Tracker,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,PTZ Tracking (ONVIF),SORT Tracker,GLM-OCR,Crop Visualization,Circle Visualization,Detections Combine,ByteTrack Tracker,SAM2 Video Tracker,Florence-2 Model,Roboflow Vision Events,Byte Tracker,Webhook Sink,Icon Visualization,Mask Area Measurement
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:
Object Detection Model,Perspective Correction,S3 Sink,Stability AI Inpainting,Image Convert Grayscale,Keypoint Detection Model,Morphological Transformation,Clip Comparison,Object Detection Model,Qwen-VL,Line Counter,QR Code Generator,Email Notification,OpenRouter,VLM As Detector,Twilio SMS/MMS Notification,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Line Counter,MoonshotAI Kimi,Polygon Zone Visualization,Image Threshold,Icon Visualization,Anthropic Claude,OpenAI,VLM As Detector,Dynamic Crop,Detections Consensus,Size Measurement,Heatmap Visualization,Keypoint Visualization,Email Notification,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Clip Comparison,Camera Focus,Label Visualization,Contrast Enhancement,Bounding Box Visualization,Local File Sink,Depth Estimation,Multi-Label Classification Model,Google Gemini,Keypoint Detection Model,Image Contours,Relative Static Crop,Motion Detection,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Background Color Visualization,Qwen 3.6 API,Single-Label Classification Model,Instance Segmentation Model,Template Matching,Qwen 3.5 API,Image Blur,Polygon Visualization,Google Gemini,SIFT Comparison,Grid Visualization,Anthropic Claude,Florence-2 Model,Triangle Visualization,Detection Event Log,Single-Label Classification Model,Roboflow Custom Metadata,OpenAI,SIFT Comparison,Slack Notification,VLM As Classifier,Image Stack,Pixelate Visualization,Stitch Images,Instance Segmentation Model,Buffer,Image Slicer,Image Preprocessing,SIFT,Line Counter Visualization,Roboflow Dataset Upload,Image Slicer,Dynamic Zone,Semantic Segmentation Model,Corner Visualization,Stability AI Outpainting,Halo Visualization,Roboflow Dataset Upload,Color Visualization,Semantic Segmentation Model,Blur Visualization,Detections List Roll-Up,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Morphological Transformation,Trace Visualization,Distance Measurement,VLM As Classifier,Reference Path Visualization,Halo Visualization,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,Identify Changes,PTZ Tracking (ONVIF),Mask Visualization,Pixel Color Count,JSON Parser,Crop Visualization,Background Subtraction,Circle Visualization,Text Display,Dimension Collapse,Absolute Static Crop,Florence-2 Model,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Identify Outliers,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Keypoint Detection Model,Path Deviation,SAM 3,Object Detection Model,Line Counter,Qwen-VL,Model Monitoring Inference Aggregator,Line Counter,Time in Zone,Stitch OCR Detections,OC-SORT Tracker,Dynamic Crop,Size Measurement,Detections Consensus,Heatmap Visualization,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Qwen3.5,Bounding Box Visualization,Overlap Filter,Detection Offset,Multi-Label Classification Model,SmolVLM2,Keypoint Detection Model,Multi-Label Classification Model,Byte Tracker,Background Color Visualization,Single-Label Classification Model,Instance Segmentation Model,Moondream2,Velocity,Per-Class Confidence Filter,Detection Event Log,Florence-2 Model,Triangle Visualization,Time in Zone,Single-Label Classification Model,Roboflow Custom Metadata,Object Detection Model,Detections Filter,Qwen2.5-VL,Detections Merge,Pixelate Visualization,Single-Label Classification Model,Instance Segmentation Model,Detections Stabilizer,Keypoint Detection Model,Roboflow Dataset Upload,Detections Classes Replacement,Semantic Segmentation Model,Segment Anything 2 Model,Corner Visualization,Multi-Label Classification Model,Roboflow Dataset Upload,Qwen3-VL,Detections Transformation,Time in Zone,Color Visualization,Blur Visualization,Detections List Roll-Up,Qwen3.5-VL,Semantic Segmentation Model,Camera Focus,Distance Measurement,Trace Visualization,Detections Stitch,Stitch OCR Detections,Byte Tracker,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,PTZ Tracking (ONVIF),SORT Tracker,GLM-OCR,Crop Visualization,Circle Visualization,Detections Combine,ByteTrack Tracker,SAM2 Video Tracker,Florence-2 Model,Roboflow Vision Events,Byte Tracker,Webhook Sink,Icon Visualization,Mask Area Measurement
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:
Object Detection Model,Perspective Correction,S3 Sink,Stability AI Inpainting,Image Convert Grayscale,Keypoint Detection Model,Morphological Transformation,Clip Comparison,Object Detection Model,Qwen-VL,Line Counter,QR Code Generator,Email Notification,OpenRouter,VLM As Detector,Twilio SMS/MMS Notification,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Line Counter,MoonshotAI Kimi,Polygon Zone Visualization,Image Threshold,Icon Visualization,Anthropic Claude,OpenAI,VLM As Detector,Dynamic Crop,Detections Consensus,Size Measurement,Heatmap Visualization,Keypoint Visualization,Email Notification,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Clip Comparison,Camera Focus,Label Visualization,Contrast Enhancement,Bounding Box Visualization,Local File Sink,Depth Estimation,Multi-Label Classification Model,Google Gemini,Keypoint Detection Model,Image Contours,Relative Static Crop,Motion Detection,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Background Color Visualization,Qwen 3.6 API,Single-Label Classification Model,Instance Segmentation Model,Template Matching,Qwen 3.5 API,Image Blur,Polygon Visualization,Google Gemini,SIFT Comparison,Grid Visualization,Anthropic Claude,Florence-2 Model,Triangle Visualization,Detection Event Log,Single-Label Classification Model,Roboflow Custom Metadata,OpenAI,SIFT Comparison,Slack Notification,VLM As Classifier,Image Stack,Pixelate Visualization,Stitch Images,Instance Segmentation Model,Buffer,Image Slicer,Image Preprocessing,SIFT,Line Counter Visualization,Roboflow Dataset Upload,Image Slicer,Dynamic Zone,Semantic Segmentation Model,Corner Visualization,Stability AI Outpainting,Halo Visualization,Roboflow Dataset Upload,Color Visualization,Semantic Segmentation Model,Blur Visualization,Detections List Roll-Up,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Morphological Transformation,Trace Visualization,Distance Measurement,VLM As Classifier,Reference Path Visualization,Halo Visualization,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,Identify Changes,PTZ Tracking (ONVIF),Mask Visualization,Pixel Color Count,JSON Parser,Crop Visualization,Background Subtraction,Circle Visualization,Text Display,Dimension Collapse,Absolute Static Crop,Florence-2 Model,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Identify Outliers,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
S3 Sink,Email Notification,Keypoint Detection Model,Morphological Transformation,Path Deviation,Qwen-VL,Clip Comparison,SAM 3,Twilio SMS/MMS Notification,YOLO-World Model,Line Counter,Time in Zone,MoonshotAI Kimi,Stitch OCR Detections,Polygon Zone Visualization,OpenAI-Compatible LLM,OpenAI,Heatmap Visualization,Email Notification,Keypoint Visualization,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Seg Preview,Google Vision OCR,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Overlap Filter,Local File Sink,Google Gemini,Byte Tracker,Background Color Visualization,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,Velocity,SIFT Comparison,Detection Event Log,Florence-2 Model,Time in Zone,Single-Label Classification Model,Detections Filter,Detections Merge,Detections Stabilizer,LMM For Classification,Image Preprocessing,Roboflow Dataset Upload,Segment Anything 2 Model,Stability AI Outpainting,Corner Visualization,Halo Visualization,Time in Zone,Semantic Segmentation Model,Blur Visualization,Detections List Roll-Up,Perception Encoder Embedding Model,Distance Measurement,Morphological Transformation,Trace Visualization,Stitch OCR Detections,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,Pixel Color Count,Text Display,Detections Combine,ByteTrack Tracker,Florence-2 Model,Byte Tracker,Icon Visualization,Mask Area Measurement,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Size Measurement,Detections Consensus,Cache Set,Bounding Box Visualization,Depth Estimation,Detection Offset,CLIP Embedding Model,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Image Blur,Anthropic Claude,Per-Class Confidence Filter,Triangle Visualization,Roboflow Custom Metadata,OpenAI,Slack Notification,Pixelate Visualization,OpenAI,Instance Segmentation Model,Line Counter Visualization,Detections Classes Replacement,Cache Get,LMM,Roboflow Dataset Upload,Detections Transformation,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Detections Stitch,Byte Tracker,Ellipse Visualization,PTZ Tracking (ONVIF),SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,CogVLM,Circle Visualization,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma
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"
}