Multi-Label Classification Model¶
v2¶
Class: RoboflowMultiLabelClassificationModelBlockV2 (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 multi-label classification 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_multi_label_classification_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. | ✅ |
disable_active_learning |
bool |
Parameter to decide if Active Learning data sampling is disabled for the model. | ✅ |
active_learning_target_dataset |
str |
Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information. | ✅ |
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 Multi-Label Classification Model in version v2.
- inputs:
Stitch Images,Pixelate Visualization,Keypoint Detection Model,SIFT Comparison,Instance Segmentation Model,Corner Visualization,Blur Visualization,Mask Visualization,Perspective Correction,SIFT,Local File Sink,Model Monitoring Inference Aggregator,Polygon Zone Visualization,Polygon Visualization,VLM as Classifier,Halo Visualization,VLM as Detector,Grid Visualization,Image Slicer,Model Comparison Visualization,Trace Visualization,Camera Focus,Detections Consensus,Image Threshold,Twilio SMS Notification,Keypoint Visualization,Email Notification,Crop Visualization,Webhook Sink,Image Preprocessing,Roboflow Custom Metadata,Roboflow Dataset Upload,Clip Comparison,Roboflow Dataset Upload,VLM as Classifier,Slack Notification,Identify Changes,SIFT Comparison,Image Blur,Relative Static Crop,Circle Visualization,Image Convert Grayscale,Dot Visualization,Background Color Visualization,Bounding Box Visualization,Ellipse Visualization,Image Contours,Label Visualization,Classification Label Visualization,Line Counter Visualization,Single-Label Classification Model,Identify Outliers,JSON Parser,Stability AI Inpainting,Reference Path Visualization,VLM as Detector,Dynamic Crop,Color Visualization,Triangle Visualization,Multi-Label Classification Model,Absolute Static Crop,Object Detection Model - outputs:
Multi-Label Classification Model,Roboflow Dataset Upload,Roboflow Dataset Upload,Keypoint Detection Model,Instance Segmentation Model,Keypoint Detection Model,Single-Label Classification Model,Classification Label Visualization,Single-Label Classification Model,Object Detection Model,Model Monitoring Inference Aggregator,Multi-Label Classification Model,Webhook Sink,Object Detection Model,Roboflow Custom Metadata,Detections Classes Replacement,Instance Segmentation Model
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Multi-Label Classification 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.disable_active_learning(boolean): Parameter to decide if Active Learning data sampling is disabled for the model.active_learning_target_dataset(roboflow_project): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
-
output
predictions(classification_prediction): Predictions from classifier.inference_id(inference_id): Inference identifier.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Multi-Label Classification Model in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_multi_label_classification_model@v2",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence": 0.3,
"disable_active_learning": true,
"active_learning_target_dataset": "my_project"
}
v1¶
Class: RoboflowMultiLabelClassificationModelBlockV1 (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 multi-label classification 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_multi_label_classification_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. | ✅ |
disable_active_learning |
bool |
Parameter to decide if Active Learning data sampling is disabled for the model. | ✅ |
active_learning_target_dataset |
str |
Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information. | ✅ |
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 Multi-Label Classification Model in version v1.
- inputs:
Stitch Images,Pixelate Visualization,Keypoint Detection Model,SIFT Comparison,Instance Segmentation Model,Corner Visualization,Blur Visualization,Mask Visualization,Perspective Correction,SIFT,Local File Sink,Model Monitoring Inference Aggregator,Polygon Zone Visualization,Polygon Visualization,VLM as Classifier,Halo Visualization,VLM as Detector,Grid Visualization,Image Slicer,Model Comparison Visualization,Trace Visualization,Camera Focus,Detections Consensus,Image Threshold,Twilio SMS Notification,Keypoint Visualization,Email Notification,Crop Visualization,Webhook Sink,Image Preprocessing,Roboflow Custom Metadata,Roboflow Dataset Upload,Clip Comparison,Roboflow Dataset Upload,VLM as Classifier,Slack Notification,Identify Changes,SIFT Comparison,Image Blur,Relative Static Crop,Circle Visualization,Image Convert Grayscale,Dot Visualization,Background Color Visualization,Bounding Box Visualization,Ellipse Visualization,Image Contours,Label Visualization,Classification Label Visualization,Line Counter Visualization,Single-Label Classification Model,Identify Outliers,JSON Parser,Stability AI Inpainting,Reference Path Visualization,VLM as Detector,Dynamic Crop,Color Visualization,Triangle Visualization,Multi-Label Classification Model,Absolute Static Crop,Object Detection Model - outputs:
Path Deviation,LMM For Classification,Line Counter,Instance Segmentation Model,CLIP Embedding Model,Mask Visualization,Line Counter,YOLO-World Model,Model Monitoring Inference Aggregator,Cache Get,Polygon Visualization,Halo Visualization,Google Vision OCR,Model Comparison Visualization,Email Notification,CogVLM,Image Threshold,Keypoint Visualization,Detections Classes Replacement,Image Preprocessing,Roboflow Dataset Upload,Slack Notification,Background Color Visualization,Bounding Box Visualization,Ellipse Visualization,Classification Label Visualization,Label Visualization,Line Counter Visualization,LMM,Stability AI Inpainting,Reference Path Visualization,Dynamic Crop,Triangle Visualization,Distance Measurement,Time in Zone,Detections Stitch,Florence-2 Model,SIFT Comparison,Corner Visualization,Perspective Correction,Local File Sink,Polygon Zone Visualization,Twilio SMS Notification,Trace Visualization,Webhook Sink,Size Measurement,OpenAI,Roboflow Custom Metadata,Cache Set,Crop Visualization,Instance Segmentation Model,Roboflow Dataset Upload,Clip Comparison,Anthropic Claude,Image Blur,Circle Visualization,Dot Visualization,Google Gemini,Segment Anything 2 Model,Florence-2 Model,Time in Zone,Path Deviation,OpenAI,Color Visualization,Pixel Color Count,Llama 3.2 Vision
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Multi-Label Classification 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.disable_active_learning(boolean): Parameter to decide if Active Learning data sampling is disabled for the model.active_learning_target_dataset(roboflow_project): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
-
output
predictions(classification_prediction): Predictions from classifier.inference_id(string): String value.
Example JSON definition of step Multi-Label Classification Model in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_multi_label_classification_model@v1",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence": 0.3,
"disable_active_learning": true,
"active_learning_target_dataset": "my_project"
}