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