Object Detection Model¶
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@v2
to 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:
Depth Estimation
,Classification Label Visualization
,Camera Calibration
,Clip Comparison
,Line Counter
,Stitch Images
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Multi-Label Classification Model
,Distance Measurement
,Twilio SMS Notification
,Object Detection Model
,SIFT Comparison
,Florence-2 Model
,OpenAI
,Label Visualization
,Roboflow Dataset Upload
,Dimension Collapse
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Pixel Color Count
,Slack Notification
,VLM as Classifier
,Detections Consensus
,Grid Visualization
,Dynamic Zone
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,Identify Changes
,VLM as Detector
,Florence-2 Model
,Local File Sink
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,SIFT
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,Background Color Visualization
,Single-Label Classification Model
,Color Visualization
,Pixelate Visualization
,Identify Outliers
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,Keypoint Detection Model
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Keypoint Visualization
,Template Matching
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Trace Visualization
,JSON Parser
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Line Counter
,Image Threshold
- outputs:
Path Deviation
,Ellipse Visualization
,Size Measurement
,Stitch OCR Detections
,Blur Visualization
,Dot Visualization
,Circle Visualization
,Background Color Visualization
,Line Counter
,Instance Segmentation Model
,Multi-Label Classification Model
,Single-Label Classification Model
,Byte Tracker
,Roboflow Dataset Upload
,Distance Measurement
,Multi-Label Classification Model
,Object Detection Model
,Color Visualization
,Detections Filter
,Byte Tracker
,Pixelate Visualization
,Florence-2 Model
,Byte Tracker
,Label Visualization
,Path Deviation
,Keypoint Detection Model
,Single-Label Classification Model
,Qwen2.5-VL
,Detections Stabilizer
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Model Comparison Visualization
,Object Detection Model
,Velocity
,Moondream2
,SmolVLM2
,Detections Consensus
,Roboflow Custom Metadata
,Webhook Sink
,Detections Merge
,Detection Offset
,Trace Visualization
,Overlap Filter
,Instance Segmentation Model
,Corner Visualization
,Segment Anything 2 Model
,Triangle Visualization
,Time in Zone
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Perspective Correction
,Detections Classes Replacement
,Dynamic Crop
,Crop Visualization
,Time in Zone
,Detections Transformation
,Line Counter
,Florence-2 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@v1
to 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:
Depth Estimation
,Classification Label Visualization
,Camera Calibration
,Clip Comparison
,Line Counter
,Stitch Images
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Multi-Label Classification Model
,Distance Measurement
,Twilio SMS Notification
,Object Detection Model
,SIFT Comparison
,Florence-2 Model
,OpenAI
,Label Visualization
,Roboflow Dataset Upload
,Dimension Collapse
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Pixel Color Count
,Slack Notification
,VLM as Classifier
,Detections Consensus
,Grid Visualization
,Dynamic Zone
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,Identify Changes
,VLM as Detector
,Florence-2 Model
,Local File Sink
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,SIFT
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,Background Color Visualization
,Single-Label Classification Model
,Color Visualization
,Pixelate Visualization
,Identify Outliers
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,Keypoint Detection Model
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Keypoint Visualization
,Template Matching
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Trace Visualization
,JSON Parser
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Line Counter
,Image Threshold
- outputs:
LMM
,Classification Label Visualization
,Stitch OCR Detections
,LMM For Classification
,Clip Comparison
,Line Counter
,Instance Segmentation Model
,CogVLM
,Byte Tracker
,Roboflow Dataset Upload
,Distance Measurement
,Twilio SMS Notification
,Florence-2 Model
,Byte Tracker
,OpenAI
,Label Visualization
,Path Deviation
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Pixel Color Count
,Detections Consensus
,Detections Merge
,Halo Visualization
,Detection Offset
,Overlap Filter
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Time in Zone
,Cache Get
,Detections Classes Replacement
,Detections Transformation
,Florence-2 Model
,Cache Set
,Local File Sink
,Path Deviation
,OpenAI
,Google Vision OCR
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,CLIP Embedding Model
,Background Color Visualization
,Color Visualization
,Detections Filter
,Byte Tracker
,Pixelate Visualization
,Google Gemini
,Stability AI Inpainting
,Polygon Zone Visualization
,Detections Stabilizer
,Keypoint Visualization
,Mask Visualization
,Velocity
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Crop Visualization
,Email Notification
,Line Counter
,Image Threshold
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"
}