LMM For Classification

Class: LMMForClassificationBlockV1

Source: inference.core.workflows.core_steps.models.foundation.lmm_classifier.v1.LMMForClassificationBlockV1

Classify an image into one or more categories using a Large Multimodal Model (LMM).

You can specify arbitrary classes to an LMMBlock.

The LLMBlock supports two LMMs:

• OpenAI's GPT-4 with Vision.

You need to provide your OpenAI API key to use the GPT-4 with Vision model.

Type identifier

Use the following identifier in step "type" field: roboflow_core/lmm_for_classification@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
lmm_type	str	Type of LMM to be used.	✅
classes	List[str]	List of classes that LMM shall classify against.	✅
lmm_config	LMMConfig	Configuration of LMM.	❌
remote_api_key	str	Holds API key required to call LMM model - in current state of development, we require OpenAI key when lmm_type=gpt_4v..	✅

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 LMM For Classification in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds LMM For Classification in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • lmm_type (string): Type of LMM to be used.
  • classes (list_of_values): List of classes that LMM shall classify against.
  • remote_api_key (Union[secret, string]): Holds API key required to call LMM model - in current state of development, we require OpenAI key when lmm_type=gpt_4v..

• output

  • raw_output (string): String value.
  • top (top_class): String value representing top class predicted by classification model.
  • parent_id (parent_id): Identifier of parent for step output.
  • root_parent_id (parent_id): Identifier of parent for step output.
  • image (image_metadata): Dictionary with image metadata required by supervision.
  • prediction_type (prediction_type): String value with type of prediction.

Example JSON definition of step LMM For Classification in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/lmm_for_classification@v1",
    "images": "$inputs.image",
    "lmm_type": "gpt_4v",
    "classes": [
        "a",
        "b"
    ],
    "lmm_config": {
        "gpt_image_detail": "low",
        "gpt_model_version": "gpt-4o",
        "max_tokens": 200
    },
    "remote_api_key": "xxx-xxx"
}