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:
Corner Visualization
,Camera Focus
,Polygon Zone Visualization
,Google Gemini
,OpenAI
,Circle Visualization
,Triangle Visualization
,Stability AI Inpainting
,Classification Label Visualization
,Bounding Box Visualization
,Depth Estimation
,SIFT
,Size Measurement
,Florence-2 Model
,Buffer
,Image Convert Grayscale
,Halo Visualization
,Dimension Collapse
,Grid Visualization
,Dynamic Zone
,Polygon Visualization
,Absolute Static Crop
,Dot Visualization
,Color Visualization
,Label Visualization
,Stability AI Outpainting
,Crop Visualization
,Perspective Correction
,Stability AI Image Generation
,Image Slicer
,OpenAI
,Image Threshold
,Image Preprocessing
,Model Comparison Visualization
,Clip Comparison
,SIFT Comparison
,Dynamic Crop
,Stitch Images
,Image Contours
,Mask Visualization
,Florence-2 Model
,Pixelate Visualization
,Llama 3.2 Vision
,Clip Comparison
,Camera Calibration
,Reference Path Visualization
,Image Blur
,Line Counter Visualization
,Anthropic Claude
,Background Color Visualization
,Image Slicer
,Keypoint Visualization
,Blur Visualization
,Ellipse Visualization
,Trace Visualization
,Relative Static Crop
- outputs:
Corner Visualization
,Keypoint Detection Model
,Polygon Zone Visualization
,Detections Classes Replacement
,Trace Visualization
,Roboflow Dataset Upload
,Gaze Detection
,Circle Visualization
,PTZ Tracking (ONVIF)
.md),Triangle Visualization
,Roboflow Custom Metadata
,Stability AI Inpainting
,Classification Label Visualization
,Single-Label Classification Model
,Bounding Box Visualization
,Single-Label Classification Model
,Template Matching
,Halo Visualization
,Dynamic Zone
,Email Notification
,Polygon Visualization
,Slack Notification
,Instance Segmentation Model
,Object Detection Model
,Dot Visualization
,Color Visualization
,Label Visualization
,Detections Consensus
,Crop Visualization
,Object Detection Model
,Perspective Correction
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Model Comparison Visualization
,SIFT Comparison
,Mask Visualization
,Webhook Sink
,Time in Zone
,Twilio SMS Notification
,Segment Anything 2 Model
,Pixelate Visualization
,Roboflow Dataset Upload
,Reference Path Visualization
,Time in Zone
,Line Counter Visualization
,Instance Segmentation Model
,Background Color Visualization
,Multi-Label Classification Model
,Keypoint Visualization
,Blur Visualization
,Ellipse Visualization
,Multi-Label Classification Model
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:
Corner Visualization
,Camera Focus
,Polygon Zone Visualization
,Google Gemini
,OpenAI
,Circle Visualization
,Triangle Visualization
,Stability AI Inpainting
,Classification Label Visualization
,Bounding Box Visualization
,Depth Estimation
,SIFT
,Size Measurement
,Florence-2 Model
,Buffer
,Image Convert Grayscale
,Halo Visualization
,Dimension Collapse
,Grid Visualization
,Dynamic Zone
,Polygon Visualization
,Absolute Static Crop
,Dot Visualization
,Color Visualization
,Label Visualization
,Stability AI Outpainting
,Crop Visualization
,Perspective Correction
,Stability AI Image Generation
,Image Slicer
,OpenAI
,Image Threshold
,Image Preprocessing
,Model Comparison Visualization
,Clip Comparison
,SIFT Comparison
,Dynamic Crop
,Stitch Images
,Image Contours
,Mask Visualization
,Florence-2 Model
,Pixelate Visualization
,Llama 3.2 Vision
,Clip Comparison
,Camera Calibration
,Reference Path Visualization
,Image Blur
,Line Counter Visualization
,Anthropic Claude
,Background Color Visualization
,Image Slicer
,Keypoint Visualization
,Blur Visualization
,Ellipse Visualization
,Trace Visualization
,Relative Static Crop
- outputs:
YOLO-World Model
,OpenAI
,Keypoint Detection Model
,Roboflow Dataset Upload
,Gaze Detection
,Circle Visualization
,PTZ Tracking (ONVIF)
.md),Roboflow Custom Metadata
,Perception Encoder Embedding Model
,Florence-2 Model
,Cache Set
,Single-Label Classification Model
,Template Matching
,Dynamic Zone
,Detections Stitch
,Instance Segmentation Model
,Color Visualization
,Detections Consensus
,Object Detection Model
,Perspective Correction
,Path Deviation
,Model Monitoring Inference Aggregator
,OpenAI
,Keypoint Detection Model
,Model Comparison Visualization
,Clip Comparison
,Dynamic Crop
,Moondream2
,Webhook Sink
,Pixelate Visualization
,Llama 3.2 Vision
,Line Counter
,Reference Path Visualization
,Time in Zone
,Local File Sink
,Image Blur
,Cache Get
,CLIP Embedding Model
,Blur Visualization
,Ellipse Visualization
,Trace Visualization
,Corner Visualization
,Polygon Zone Visualization
,Detections Classes Replacement
,Google Gemini
,OpenAI
,Triangle Visualization
,Stability AI Inpainting
,Classification Label Visualization
,Single-Label Classification Model
,Bounding Box Visualization
,Size Measurement
,Distance Measurement
,CogVLM
,Halo Visualization
,LMM
,Email Notification
,Polygon Visualization
,Slack Notification
,Object Detection Model
,Dot Visualization
,Label Visualization
,Stability AI Outpainting
,Crop Visualization
,Google Vision OCR
,Stability AI Image Generation
,Image Threshold
,Pixel Color Count
,Image Preprocessing
,SIFT Comparison
,Mask Visualization
,Time in Zone
,Twilio SMS Notification
,Segment Anything 2 Model
,Florence-2 Model
,Roboflow Dataset Upload
,Line Counter Visualization
,Path Deviation
,Instance Segmentation Model
,Background Color Visualization
,Anthropic Claude
,LMM For Classification
,Line Counter
,Multi-Label Classification Model
,Keypoint Visualization
,Multi-Label Classification Model
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"
]
]
}