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