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

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[string, secret]): 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"
}