VLM as Detector¶
v2¶
Class: VLMAsDetectorBlockV2
(there are multiple versions of this block)
Source: inference.core.workflows.core_steps.formatters.vlm_as_detector.v2.VLMAsDetectorBlockV2
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 object-detection prediction and returned as block output.
Accepted formats:
-
valid JSON strings
-
JSON documents wrapped with Markdown tags
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_detector@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.. | ✅ |
model_type |
str |
Type of the model that generated prediction. | ❌ |
task_type |
str |
Task type to performed by model.. | ❌ |
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 Detector
in version v2
.
- inputs:
Blur Visualization
,Polygon Visualization
,Triangle Visualization
,Anthropic Claude
,Trace Visualization
,Size Measurement
,Label Visualization
,Clip Comparison
,Perspective Correction
,Camera Calibration
,OpenAI
,Absolute Static Crop
,Image Preprocessing
,Relative Static Crop
,Bounding Box Visualization
,Image Threshold
,Reference Path Visualization
,Buffer
,Stability AI Outpainting
,SIFT
,Camera Focus
,OpenAI
,Dynamic Crop
,Depth Estimation
,Halo Visualization
,Dimension Collapse
,Stability AI Inpainting
,Background Color Visualization
,Dot Visualization
,Florence-2 Model
,Classification Label Visualization
,SIFT Comparison
,Circle Visualization
,Image Blur
,Keypoint Visualization
,Stability AI Image Generation
,Florence-2 Model
,Google Gemini
,Image Convert Grayscale
,Line Counter Visualization
,Model Comparison Visualization
,Ellipse Visualization
,Dynamic Zone
,Image Contours
,Llama 3.2 Vision
,Image Slicer
,Crop Visualization
,Corner Visualization
,Clip Comparison
,Grid Visualization
,Pixelate Visualization
,Stitch Images
,Image Slicer
,Mask Visualization
,Color Visualization
,Polygon Zone Visualization
- outputs:
Blur Visualization
,Triangle Visualization
,Trace Visualization
,Label Visualization
,Distance Measurement
,Model Monitoring Inference Aggregator
,Roboflow Dataset Upload
,Time in Zone
,Keypoint Detection Model
,Byte Tracker
,Reference Path Visualization
,Segment Anything 2 Model
,Slack Notification
,Instance Segmentation Model
,Roboflow Dataset Upload
,Detection Offset
,Stability AI Inpainting
,Background Color Visualization
,Byte Tracker
,Detections Stabilizer
,Detections Transformation
,Circle Visualization
,Keypoint Visualization
,PTZ Tracking (ONVIF)
.md),Line Counter Visualization
,Multi-Label Classification Model
,Model Comparison Visualization
,Path Deviation
,Dynamic Zone
,Roboflow Custom Metadata
,Stitch OCR Detections
,Crop Visualization
,Corner Visualization
,Line Counter
,Multi-Label Classification Model
,Pixelate Visualization
,Velocity
,Overlap Filter
,Mask Visualization
,Color Visualization
,Polygon Visualization
,Email Notification
,Keypoint Detection Model
,Single-Label Classification Model
,Gaze Detection
,Size Measurement
,Perspective Correction
,Detections Consensus
,Path Deviation
,Detections Filter
,Detections Merge
,Bounding Box Visualization
,Detections Stitch
,Instance Segmentation Model
,Twilio SMS Notification
,Detections Classes Replacement
,Dynamic Crop
,Halo Visualization
,Florence-2 Model
,Dot Visualization
,Template Matching
,Classification Label Visualization
,Webhook Sink
,Time in Zone
,SIFT Comparison
,Florence-2 Model
,Object Detection Model
,Ellipse Visualization
,Line Counter
,Single-Label Classification Model
,Byte Tracker
,Object Detection Model
,Polygon Zone Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
VLM as Detector
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
(object_detection_prediction
): Prediction with detected bounding boxes in form of sv.Detections(...) object.inference_id
(inference_id
): Inference identifier.
Example JSON definition of step VLM as Detector
in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/vlm_as_detector@v2",
"image": "$inputs.image",
"vlm_output": [
"$steps.lmm.output"
],
"classes": [
"$steps.lmm.classes",
"$inputs.classes",
[
"class_a",
"class_b"
]
],
"model_type": [
"google-gemini",
"anthropic-claude",
"florence-2"
],
"task_type": "<block_does_not_provide_example>"
}
v1¶
Class: VLMAsDetectorBlockV1
(there are multiple versions of this block)
Source: inference.core.workflows.core_steps.formatters.vlm_as_detector.v1.VLMAsDetectorBlockV1
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 object-detection prediction and returned as block output.
Accepted formats:
-
valid JSON strings
-
JSON documents wrapped with Markdown tags
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_detector@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.. | ✅ |
model_type |
str |
Type of the model that generated prediction. | ❌ |
task_type |
str |
Task type to performed by model.. | ❌ |
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 Detector
in version v1
.
- inputs:
Blur Visualization
,Polygon Visualization
,Triangle Visualization
,Anthropic Claude
,Trace Visualization
,Size Measurement
,Label Visualization
,Clip Comparison
,Perspective Correction
,Camera Calibration
,OpenAI
,Absolute Static Crop
,Image Preprocessing
,Relative Static Crop
,Bounding Box Visualization
,Image Threshold
,Reference Path Visualization
,Buffer
,Stability AI Outpainting
,SIFT
,Camera Focus
,OpenAI
,Dynamic Crop
,Depth Estimation
,Halo Visualization
,Dimension Collapse
,Stability AI Inpainting
,Background Color Visualization
,Dot Visualization
,Florence-2 Model
,Classification Label Visualization
,SIFT Comparison
,Circle Visualization
,Image Blur
,Keypoint Visualization
,Stability AI Image Generation
,Florence-2 Model
,Google Gemini
,Image Convert Grayscale
,Line Counter Visualization
,Model Comparison Visualization
,Ellipse Visualization
,Dynamic Zone
,Image Contours
,Llama 3.2 Vision
,Image Slicer
,Crop Visualization
,Corner Visualization
,Clip Comparison
,Grid Visualization
,Pixelate Visualization
,Stitch Images
,Image Slicer
,Mask Visualization
,Color Visualization
,Polygon Zone Visualization
- outputs:
Blur Visualization
,Triangle Visualization
,Anthropic Claude
,Trace Visualization
,YOLO-World Model
,Label Visualization
,Distance Measurement
,LMM
,Model Monitoring Inference Aggregator
,Roboflow Dataset Upload
,Time in Zone
,Pixel Color Count
,Image Preprocessing
,Keypoint Detection Model
,Byte Tracker
,Image Threshold
,Reference Path Visualization
,Segment Anything 2 Model
,Slack Notification
,Stability AI Outpainting
,Instance Segmentation Model
,Roboflow Dataset Upload
,Detection Offset
,Google Vision OCR
,Stability AI Inpainting
,Background Color Visualization
,Byte Tracker
,Detections Stabilizer
,Detections Transformation
,Circle Visualization
,Keypoint Visualization
,Image Blur
,Google Gemini
,OpenAI
,PTZ Tracking (ONVIF)
.md),Line Counter Visualization
,Multi-Label Classification Model
,Model Comparison Visualization
,Path Deviation
,Dynamic Zone
,Perception Encoder Embedding Model
,Roboflow Custom Metadata
,Stitch OCR Detections
,Crop Visualization
,Corner Visualization
,Line Counter
,Multi-Label Classification Model
,Pixelate Visualization
,Local File Sink
,Velocity
,Overlap Filter
,Cache Set
,Mask Visualization
,Color Visualization
,Clip Comparison
,Polygon Visualization
,Email Notification
,Keypoint Detection Model
,Single-Label Classification Model
,Gaze Detection
,Size Measurement
,Perspective Correction
,Detections Consensus
,Path Deviation
,OpenAI
,Detections Filter
,Detections Merge
,Bounding Box Visualization
,Detections Stitch
,Instance Segmentation Model
,Twilio SMS Notification
,CogVLM
,OpenAI
,Detections Classes Replacement
,Dynamic Crop
,Halo Visualization
,Florence-2 Model
,Dot Visualization
,Template Matching
,Classification Label Visualization
,Webhook Sink
,Time in Zone
,SIFT Comparison
,Stability AI Image Generation
,Florence-2 Model
,LMM For Classification
,Object Detection Model
,Ellipse Visualization
,Llama 3.2 Vision
,Line Counter
,Single-Label Classification Model
,CLIP Embedding Model
,Byte Tracker
,Object Detection Model
,Cache Get
,Polygon Zone Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
VLM as Detector
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
(object_detection_prediction
): Prediction with detected bounding boxes in form of sv.Detections(...) object.inference_id
(string
): String value.
Example JSON definition of step VLM as Detector
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/vlm_as_detector@v1",
"image": "$inputs.image",
"vlm_output": [
"$steps.lmm.output"
],
"classes": [
"$steps.lmm.classes",
"$inputs.classes",
[
"class_a",
"class_b"
]
],
"model_type": [
"google-gemini",
"anthropic-claude",
"florence-2"
],
"task_type": "<block_does_not_provide_example>"
}