How do I use attribute-classifier-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@attribute-classifier-mcp Detect objects in /data/test.jpg and classify each one's material and color" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

attribute-classifier-mcp

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Overview Schema Source Related Servers Discussions Admin

Python

Local

Attribute Classifier MCP Server

🎯 YOLO Detection → Object Cropping → CNN Attribute Classification → JSON Results

A Model Context Protocol (MCP) server that combines YOLO object detection with per-attribute CNN classifiers. Detects objects in images, crops them, and classifies each object's visual attributes (material, color, has_armrest, etc.) using dedicated CNN models.

✨ Features

Feature	Description
📦 Object Cropping	Crop objects from images using YOLO txt bbox annotations
🗂️ Dataset Organization	Organize cropped images by attribute value for CNN training
🧠 CNN Training	Train dedicated CNN classifiers per attribute (ResNet, MobileNet, EfficientNet)
🔍 Full Pipeline Inference	YOLO detect → crop → multi-attribute CNN classify → JSON output
🔄 Hot-swap Models	Register/replace attribute models without restarting
📋 Attribute Discovery	Auto-discover attributes from X-AnyLabeling JSON annotations
⚡ Batch Processing	Process multiple images in one call
⚙️ Configurable	YAML config for paths, training params, YOLO settings

Related MCP server: Kolosal Vision MCP

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/your-username/attribute-classifier-mcp.git
cd attribute-classifier-mcp

# Install dependencies
pip install -r requirements.txt

# Or install as package
pip install -e .

Configure MCP Client

Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "attribute-classifier": {
      "command": "python",
      "args": ["src/server.py"],
      "cwd": "/path/to/attribute-classifier-mcp"
    }
  }
}

With Docker

docker build -t attribute-classifier-mcp .
docker run -v /your/data:/app/data -v /your/models:/app/models attribute-classifier-mcp

📊 Complete Workflow

Step 1: Prepare Your Data

data/
├── images/          # Original images (.jpg, .png)
├── labels/          # YOLO txt annotations (one per image)
│   └── img001.txt   # "0 0.5 0.5 0.3 0.4" (class cx cy w h)
├── jsons/           # X-AnyLabeling JSON attributes (one per image)
│   └── img001.json  # {shapes: [{label, attributes: {material: "leather"}}]}
├── classes.txt      # Class names (one per line)
└── attributes.json  # Optional: master JSON with all attributes

Step 2: Crop Objects

Use the crop_objects tool to crop all annotated objects from images:

{
  "tool": "crop_objects",
  "arguments": {
    "images_dir": "./data/images",
    "labels_dir": "./data/labels",
    "json_dir": "./data/jsons",
    "classes_file": "./data/classes.txt"
  }
}

Step 3: Organize by Attribute

Organize crops into classification datasets:

{
  "tool": "organize_dataset",
  "arguments": {
    "cropped_dir": "./data/cropped",
    "attribute_name": "material",
    "master_json": "./data/attributes.json"
  }
}

This creates:

data/datasets/material/
├── leather/     # Images of leather objects
├── wood/        # Images of wooden objects
├── fabric/      # Images of fabric objects
└── metal/       # Images of metal objects

Step 4: Train Models

Train a CNN for each attribute:

{
  "tool": "train_attribute_model",
  "arguments": {
    "dataset_dir": "./data/datasets/material",
    "attribute_name": "material",
    "backbone": "resnet18",
    "epochs": 50
  }
}

Or train all attributes at once:

{
  "tool": "train_all_attributes",
  "arguments": {
    "datasets_dir": "./data/datasets"
  }
}

Step 5: Inference

Run the full pipeline on new images:

{
  "tool": "detect_and_classify",
  "arguments": {
    "image_path": "./test.jpg"
  }
}

Output:

{
  "image": "test.jpg",
  "image_size": {"width": 1920, "height": 1080},
  "total_objects": 2,
  "detections": [
    {
      "index": 0,
      "class_name": "chair",
      "confidence": 0.92,
      "bbox": {"x1": 100, "y1": 200, "x2": 400, "y2": 600},
      "attributes": {
        "material": {
          "attribute": "material",
          "predicted_value": "leather",
          "confidence": 0.95,
          "all_probabilities": {
            "leather": 0.95,
            "wood": 0.03,
            "fabric": 0.02
          }
        },
        "has_armrest": {
          "attribute": "has_armrest",
          "predicted_value": "yes",
          "confidence": 0.88,
          "all_probabilities": {"yes": 0.88, "no": 0.12}
        }
      }
    }
  ]
}

🛠️ All MCP Tools (16 total)

Data Preparation

Tool	Description
`crop_objects`	Crop objects from images using YOLO txt bbox annotations
`organize_dataset`	Organize crops by attribute value into classification folders
`discover_attributes`	Discover all attribute names and values from JSON annotations

Training

Tool	Description
`train_attribute_model`	Train a CNN classifier for one attribute
`train_all_attributes`	Train CNN classifiers for all discovered attributes

Inference

Tool	Description
`detect_and_classify`	Full pipeline: YOLO → crop → CNN classify → JSON
`classify_crop`	Classify a pre-cropped image through attribute models
`batch_detect_and_classify`	Process multiple images through the full pipeline

Model Management

Tool	Description
`register_model`	Register/replace a CNN model for an attribute
`unregister_model`	Remove a model from the registry
`list_models`	List all registered attribute models
`get_model_info`	Get detailed info about a specific model

Configuration

Tool	Description
`update_config`	Update server configuration
`get_config`	Get current configuration
`set_class_attributes`	Map detected classes to their attributes
`full_pipeline`	Run complete pipeline: crop → organize → train

🔧 Configuration

Edit config/default_config.yaml or pass a custom config:

python src/server.py /path/to/my_config.yaml

Key Config Options

# Paths
paths:
  images_dir: "./data/images"
  labels_dir: "./data/labels"
  json_dir: "./data/jsons"
  master_json: "./data/attributes.json"

# Training
training:
  backbone: "resnet18"    # resnet18/34/50, mobilenet_v2, efficientnet_b0
  epochs: 50
  batch_size: 32
  device: "auto"          # auto, cpu, cuda

# YOLO
yolo:
  model: "yolov8n.pt"
  confidence: 0.5

🔄 Hot-swap Models

Replace any attribute's model at runtime:

{
  "tool": "register_model",
  "arguments": {
    "attribute_name": "material",
    "model_path": "./models/material_efficientnet_b0_best.pth"
  }
}

📁 Supported Annotation Formats

YOLO TXT (for bounding boxes)

0 0.5125 0.4833 0.3250 0.4167
1 0.2500 0.7500 0.2000 0.3000

X-AnyLabeling JSON (for attributes)

{
  "shapes": [
    {
      "label": "chair",
      "points": [[100, 200], [400, 600]],
      "shape_type": "rectangle",
      "flags": {"has_armrest": true},
      "attributes": {"material": "leather", "seat_count": "1"}
    }
  ]
}

📜 License

MIT License

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