{
    "_comment": "Image Classification Dataset - Metadata | Author: Molla Samser | https://rskworld.in",
    "dataset_info": {
        "name": "Image Classification Dataset",
        "version": "1.0.0",
        "author": "Molla Samser",
        "email": "help@rskworld.in",
        "website": "https://rskworld.in",
        "license": "MIT",
        "created_date": "2025-01-01",
        "last_updated": "2025-12-25"
    },
    "statistics": {
        "total_images": 10000,
        "total_categories": 15,
        "splits": {
            "train": {
                "images": 7000,
                "percentage": 70
            },
            "validation": {
                "images": 1500,
                "percentage": 15
            },
            "test": {
                "images": 1500,
                "percentage": 15
            }
        },
        "image_formats": ["jpg", "jpeg", "png"],
        "average_resolution": {
            "width": 224,
            "height": 224
        },
        "color_mode": "RGB",
        "total_size_mb": 500
    },
    "categories": [
        {
            "id": 0,
            "name": "animals",
            "display_name": "Animals",
            "description": "Images of various animals including dogs, cats, birds, etc.",
            "count": 1500,
            "subcategories": ["dogs", "cats", "birds", "fish", "wildlife"]
        },
        {
            "id": 1,
            "name": "vehicles",
            "display_name": "Vehicles",
            "description": "Images of cars, trucks, motorcycles, and other vehicles.",
            "count": 1200,
            "subcategories": ["cars", "trucks", "motorcycles", "bicycles", "buses"]
        },
        {
            "id": 2,
            "name": "nature",
            "display_name": "Nature",
            "description": "Natural landscapes, forests, mountains, and scenery.",
            "count": 1000,
            "subcategories": ["forests", "mountains", "beaches", "deserts", "lakes"]
        },
        {
            "id": 3,
            "name": "food",
            "display_name": "Food",
            "description": "Various food items and dishes.",
            "count": 800,
            "subcategories": ["fruits", "vegetables", "dishes", "desserts", "beverages"]
        },
        {
            "id": 4,
            "name": "buildings",
            "display_name": "Buildings",
            "description": "Architecture, houses, and structures.",
            "count": 900,
            "subcategories": ["houses", "skyscrapers", "monuments", "bridges", "temples"]
        },
        {
            "id": 5,
            "name": "fashion",
            "display_name": "Fashion",
            "description": "Clothing, accessories, and fashion items.",
            "count": 700,
            "subcategories": ["shirts", "dresses", "shoes", "accessories", "bags"]
        },
        {
            "id": 6,
            "name": "aircraft",
            "display_name": "Aircraft",
            "description": "Airplanes, helicopters, and flying vehicles.",
            "count": 600,
            "subcategories": ["airplanes", "helicopters", "drones", "jets", "balloons"]
        },
        {
            "id": 7,
            "name": "sports",
            "display_name": "Sports",
            "description": "Sports equipment and activities.",
            "count": 800,
            "subcategories": ["football", "basketball", "tennis", "swimming", "cricket"]
        },
        {
            "id": 8,
            "name": "instruments",
            "display_name": "Musical Instruments",
            "description": "Various musical instruments.",
            "count": 500,
            "subcategories": ["guitar", "piano", "drums", "violin", "flute"]
        },
        {
            "id": 9,
            "name": "electronics",
            "display_name": "Electronics",
            "description": "Electronic devices and gadgets.",
            "count": 700,
            "subcategories": ["phones", "laptops", "cameras", "tablets", "headphones"]
        },
        {
            "id": 10,
            "name": "furniture",
            "display_name": "Furniture",
            "description": "Home and office furniture.",
            "count": 600,
            "subcategories": ["chairs", "tables", "sofas", "beds", "desks"]
        },
        {
            "id": 11,
            "name": "plants",
            "display_name": "Plants",
            "description": "Flowers, trees, and vegetation.",
            "count": 700,
            "subcategories": ["flowers", "trees", "succulents", "herbs", "indoor_plants"]
        },
        {
            "id": 12,
            "name": "people",
            "display_name": "People",
            "description": "Human figures and portraits.",
            "count": 500,
            "subcategories": ["portraits", "groups", "activities", "professions"]
        },
        {
            "id": 13,
            "name": "art",
            "display_name": "Art",
            "description": "Paintings, sculptures, and artistic works.",
            "count": 400,
            "subcategories": ["paintings", "sculptures", "drawings", "digital_art"]
        },
        {
            "id": 14,
            "name": "tools",
            "display_name": "Tools",
            "description": "Hand tools and equipment.",
            "count": 400,
            "subcategories": ["hand_tools", "power_tools", "gardening", "kitchen"]
        }
    ],
    "preprocessing": {
        "resize": [224, 224],
        "normalization": {
            "method": "min_max",
            "range": [0, 1]
        },
        "augmentation_applied": false
    },
    "recommended_models": [
        "ResNet50",
        "VGG16",
        "EfficientNetB0",
        "MobileNetV2",
        "InceptionV3"
    ],
    "benchmark_results": {
        "ResNet50": {
            "accuracy": 0.94,
            "f1_score": 0.93,
            "training_time_hours": 2.5
        },
        "VGG16": {
            "accuracy": 0.92,
            "f1_score": 0.91,
            "training_time_hours": 3.0
        },
        "EfficientNetB0": {
            "accuracy": 0.95,
            "f1_score": 0.94,
            "training_time_hours": 1.8
        }
    }
}