VLM as Classifier¶
v2¶
Class: VLMAsClassifierBlockV2
(there are multiple versions of this block)
Source: inference.core.workflows.core_steps.formatters.vlm_as_classifier.v2.VLMAsClassifierBlockV2
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
The block expects string input that would be produced by blocks exposing Large Language Models (LLMs) and Visual Language Models (VLMs). Input is parsed to classification prediction and returned as block output.
Accepted formats:
-
valid JSON strings
-
JSON documents wrapped with Markdown tags (very common for GPT responses)
Example:
{"my": "json"}
Details regarding block behavior:
-
error_status
is setTrue
whenever parsing cannot be completed -
in case of multiple markdown blocks with raw JSON content - only first will be parsed
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/vlm_as_classifier@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.. | ❌ |
classes |
List[str] |
List of all classes used by the model, required to generate mapping between class name and class id.. | ✅ |
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 VLM as Classifier
in version v2
.
- inputs:
OpenAI
,Size Measurement
,Buffer
,Image Convert Grayscale
,Florence-2 Model
,Absolute Static Crop
,Relative Static Crop
,Label Visualization
,Line Counter Visualization
,Background Color Visualization
,Stitch Images
,Camera Focus
,Image Contours
,Image Preprocessing
,Image Slicer
,Reference Path Visualization
,SIFT Comparison
,Triangle Visualization
,Grid Visualization
,Polygon Zone Visualization
,Keypoint Visualization
,Depth Estimation
,Llama 3.2 Vision
,Dynamic Zone
,Bounding Box Visualization
,Image Blur
,Perspective Correction
,Halo Visualization
,Google Gemini
,Ellipse Visualization
,Color Visualization
,Crop Visualization
,Clip Comparison
,Dot Visualization
,Pixelate Visualization
,Model Comparison Visualization
,Classification Label Visualization
,Camera Calibration
,Dimension Collapse
,Stability AI Image Generation
,Polygon Visualization
,Trace Visualization
,Corner Visualization
,Image Threshold
,Blur Visualization
,Image Slicer
,Clip Comparison
,Stability AI Inpainting
,Mask Visualization
,SIFT
,Anthropic Claude
,Florence-2 Model
,Circle Visualization
,Dynamic Crop
- outputs:
Roboflow Custom Metadata
,Multi-Label Classification Model
,Twilio SMS Notification
,Label Visualization
,Gaze Detection
,Line Counter Visualization
,Detections Classes Replacement
,Background Color Visualization
,Keypoint Detection Model
,Reference Path Visualization
,Instance Segmentation Model
,SIFT Comparison
,Template Matching
,Object Detection Model
,Triangle Visualization
,Polygon Zone Visualization
,Multi-Label Classification Model
,Keypoint Visualization
,Roboflow Dataset Upload
,Bounding Box Visualization
,Perspective Correction
,Halo Visualization
,Object Detection Model
,Ellipse Visualization
,Color Visualization
,Crop Visualization
,Webhook Sink
,Dot Visualization
,Pixelate Visualization
,Single-Label Classification Model
,Model Comparison Visualization
,Email Notification
,Classification Label Visualization
,Instance Segmentation Model
,Slack Notification
,Roboflow Dataset Upload
,Polygon Visualization
,Segment Anything 2 Model
,Time in Zone
,Time in Zone
,Trace Visualization
,Corner Visualization
,Single-Label Classification Model
,Blur Visualization
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Mask Visualization
,Circle Visualization
,Detections Consensus
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
VLM as Classifier
in version v2
has.
Bindings
-
input
image
(image
): The image which was the base to generate VLM prediction.vlm_output
(language_model_output
): The string with raw classification prediction to parse..classes
(list_of_values
): List of all classes used by the model, required to generate mapping between class name and class id..
-
output
error_status
(boolean
): Boolean flag.predictions
(classification_prediction
): Predictions from classifier.inference_id
(inference_id
): Inference identifier.
Example JSON definition of step VLM as Classifier
in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/vlm_as_classifier@v2",
"image": "$inputs.image",
"vlm_output": [
"$steps.lmm.output"
],
"classes": [
"$steps.lmm.classes",
"$inputs.classes",
[
"class_a",
"class_b"
]
]
}
v1¶
Class: VLMAsClassifierBlockV1
(there are multiple versions of this block)
Source: inference.core.workflows.core_steps.formatters.vlm_as_classifier.v1.VLMAsClassifierBlockV1
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
The block expects string input that would be produced by blocks exposing Large Language Models (LLMs) and Visual Language Models (VLMs). Input is parsed to classification prediction and returned as block output.
Accepted formats:
-
valid JSON strings
-
JSON documents wrapped with Markdown tags (very common for GPT responses)
Example:
{"my": "json"}
Details regarding block behavior:
-
error_status
is setTrue
whenever parsing cannot be completed -
in case of multiple markdown blocks with raw JSON content - only first will be parsed
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/vlm_as_classifier@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.. | ❌ |
classes |
List[str] |
List of all classes used by the model, required to generate mapping between class name and class id.. | ✅ |
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 VLM as Classifier
in version v1
.
- inputs:
OpenAI
,Size Measurement
,Buffer
,Image Convert Grayscale
,Florence-2 Model
,Absolute Static Crop
,Relative Static Crop
,Label Visualization
,Line Counter Visualization
,Background Color Visualization
,Stitch Images
,Camera Focus
,Image Contours
,Image Preprocessing
,Image Slicer
,Reference Path Visualization
,SIFT Comparison
,Triangle Visualization
,Grid Visualization
,Polygon Zone Visualization
,Keypoint Visualization
,Depth Estimation
,Llama 3.2 Vision
,Dynamic Zone
,Bounding Box Visualization
,Image Blur
,Perspective Correction
,Halo Visualization
,Google Gemini
,Ellipse Visualization
,Color Visualization
,Crop Visualization
,Clip Comparison
,Dot Visualization
,Pixelate Visualization
,Model Comparison Visualization
,Classification Label Visualization
,Camera Calibration
,Dimension Collapse
,Stability AI Image Generation
,Polygon Visualization
,Trace Visualization
,Corner Visualization
,Image Threshold
,Blur Visualization
,Image Slicer
,Clip Comparison
,Stability AI Inpainting
,Mask Visualization
,SIFT
,Anthropic Claude
,Florence-2 Model
,Circle Visualization
,Dynamic Crop
- outputs:
Size Measurement
,Roboflow Custom Metadata
,LMM
,Multi-Label Classification Model
,Distance Measurement
,Line Counter Visualization
,Gaze Detection
,Detections Classes Replacement
,Background Color Visualization
,Keypoint Detection Model
,Reference Path Visualization
,Instance Segmentation Model
,SIFT Comparison
,Object Detection Model
,Triangle Visualization
,Path Deviation
,Line Counter
,Multi-Label Classification Model
,Llama 3.2 Vision
,Roboflow Dataset Upload
,Google Vision OCR
,Clip Comparison
,Perspective Correction
,Object Detection Model
,Crop Visualization
,Webhook Sink
,Dot Visualization
,Model Comparison Visualization
,Email Notification
,Classification Label Visualization
,Instance Segmentation Model
,Slack Notification
,Stability AI Image Generation
,Cache Set
,Time in Zone
,Time in Zone
,Trace Visualization
,Corner Visualization
,Line Counter
,Image Threshold
,Blur Visualization
,Local File Sink
,CogVLM
,Stability AI Inpainting
,Keypoint Detection Model
,Circle Visualization
,OpenAI
,Path Deviation
,Florence-2 Model
,Twilio SMS Notification
,Label Visualization
,Image Preprocessing
,Detections Stitch
,Template Matching
,Polygon Zone Visualization
,Keypoint Visualization
,LMM For Classification
,Bounding Box Visualization
,Image Blur
,OpenAI
,Halo Visualization
,CLIP Embedding Model
,Ellipse Visualization
,Color Visualization
,Google Gemini
,Pixelate Visualization
,Single-Label Classification Model
,Pixel Color Count
,YOLO-World Model
,Roboflow Dataset Upload
,Polygon Visualization
,Segment Anything 2 Model
,Single-Label Classification Model
,Cache Get
,Model Monitoring Inference Aggregator
,Mask Visualization
,Anthropic Claude
,Florence-2 Model
,Dynamic Crop
,Detections Consensus
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
VLM as Classifier
in version v1
has.
Bindings
-
input
image
(image
): The image which was the base to generate VLM prediction.vlm_output
(language_model_output
): The string with raw classification prediction to parse..classes
(list_of_values
): List of all classes used by the model, required to generate mapping between class name and class id..
-
output
error_status
(boolean
): Boolean flag.predictions
(classification_prediction
): Predictions from classifier.inference_id
(string
): String value.
Example JSON definition of step VLM as Classifier
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/vlm_as_classifier@v1",
"image": "$inputs.image",
"vlm_output": [
"$steps.lmm.output"
],
"classes": [
"$steps.lmm.classes",
"$inputs.classes",
[
"class_a",
"class_b"
]
]
}