National Flowers Dataset

National Flowers Dataset

Datasets

National Flowers Dataset

File

National Flowers Dataset

Use Case

Image Classification

Description

This data set contains 9 different types of flowers. Your task is to build a machine-learning or deep-learning model that can classify these images as flowers.

National Flowers Dataset

About Dataset

This data set contains 9 different types of flowers. Your task is to build a machine-learning or deep-learning model that can classify these images as flowers. These classes are:

  1. Tulip
  2. Sunflower
  3. Rose
  4. Orchid
  5. Lotus
  6. Lilly
  7. Lavender
  8. Dandelion
  9. Daisy

    Composition of the Dataset

    The dataset includes high-resolution images of national flowers, capturing various aspects such as close-ups of petals, full blooms, and different angles. Each image is annotated with detailed metadata, including the flower’s scientific name, common name, country of origin, and cultural significance. The diverse range of flowers in the dataset provides a rich resource for understanding botanical diversity and cultural heritage.

    Applications in Research and Development

    The National Flowers Dataset has several important applications:

    • Botanical Research: Facilitates the study of plant morphology and taxonomy, aiding in the identification and classification of flowers.
    • Cultural Studies: Helps researchers explore the cultural significance and symbolism of national flowers in different countries.
    • Computer Vision: Supports the development of AI models for flower recognition, classification, and image-based search systems, enhancing applications in gardening apps and educational tools.

    Significance

    This dataset is invaluable for educators, researchers, and developers aiming to explore the intersection of botany and culture. By providing a diverse collection of national flowers, it aids in promoting botanical education and preserving cultural heritage. The dataset’s high-quality images and detailed annotations make it a crucial resource for advancing research and technology in related fields.

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