Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.

The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories.

Open source flower images available in Python distribution. Raw images converted to TFRecord format in offline process.

Flowers Classification

## Overview

Flowers Classification is a dataset for classification tasks - it contains Names Flowertype annotations for 3,667 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for "oxford-flowers"

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This dataset is a mix of the Capybara, Open-Platypus-Commercial and Wizard-Vicuna-Unfiltered datasets. As such, it can be used for commercial purposes. These base datasets provide a strong reasoning background on multiple fields of human knowledge, and that's why I chose all of these.

  Dataset Details





  Dataset Description

Curated by: Thermostatic Funded by [optional]: Thermostatic Shared by [optional]: Thermostatic Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/Thermostatic/flowers.

The Oxford Flower 102 dataset is a popular collection of images specifically designed for flower image classification tasks. Here's a breakdown of its key characteristics:

• Classes: 102 flower categories, commonly found in the United Kingdom.
• Image Count: Over 8000 images spread across the classes. Each class has between 40 and 258 images.
• Variations: Images exhibit variations in scale, pose (orientation of the flower), and lighting conditions.
• Challenges: The dataset includes categories with high internal variations (flowers of the same species can look quite different) and some classes that are very similar. This reflects real-world scenarios where flower identification can be nuanced.

This variety in the Oxford Flower 102 dataset makes it a valuable resource for training robust image classification models that can handle real-world complexities.

Monarda Fistulosa Flowers

## Overview

Monarda Fistulosa Flowers is a dataset for object detection tasks - it contains Objects annotations for 436 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for "flowers-dataset"

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Overview

The Flowers dataset is a classification detection dataset various flower species like dandelions and daisies.

Example Image: https://i.imgur.com/LsBKyoS.png" alt="Example Image">

Use Cases

Build a flower classifier model! Consider deploying that to a mobile app for outdoor enthusiasts or florist hobbyists.

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Introduction

This is a version of the VGG Flowers dataset introduced in [1].

Usage

The train, val and test split are given in the respective json files in this GitHub repo, in this folder. Please refer to the README file of the repository for the folder structure.

These files can be used with any framework of choice.

References

[1] Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)

IMFI: Indian Medicinal Flower Image Dataset is a well compiled dataset consisting of high-quality images of medicinal flowers found in the Indian geographical area. These flowers have medicinal properties and are used in traditional and natural medicinal practices. The dataset is divided into 28 classes of flowers and has 6,316 images in total. All the images in the dataset are cropped and are in equal ratios of dimension (1:1). The data was gathered from various parts of Kerala and Karnataka, India.

flowersdataset #segmentation #VGG

  Dataset Card for Flowers Dataset





  Dataset Summary

VGG have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3… See the full description on the dataset page: https://huggingface.co/datasets/Guldeniz/flower_dataset.

huggan/flowers-102-categories dataset hosted on Hugging Face and contributed by the HF Datasets community

full sun still images at +/- 10 degree solar angle 11am-1pm in summer 2021. insect vision monochrome images of strawberry cultivars: S=Seascape, H= Heacker, FL= Fort laramie, BTR= Berried treasure reed, V= vesca.

AI Detect Flowers

## Overview

AI Detect Flowers is a dataset for object detection tasks - it contains Daisy Dandelion Roses Sunflowers annotations for 488 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This project is meant to help users identify polinator flowers which can be found in Maryland! Since COVID, people have been outside and identifying flowers and plants more than ever so I am working on this model to support the community in flower identification. This model was created for a class assignment in AI and Natural History at St. Mary’s College of Maryland.

Dataset

This dataset was created by ML Engineer

Contents

According to Cognitive Market Research, the global Preserved Flowers Market size will be USD 189.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 5.50% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 70.08 million in 2025 and will grow at a compound annual growth rate (CAGR) of 3.3% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue with a market size of USD 54.93 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 45.46 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue with a market size of USD 7.20 million in 2025 and will grow at a compound annual growth rate (CAGR) of 4.5% from 2025 to 2033.
Middle East had a market share of around 4.00% of the global revenue and was estimated at a market size of USD 7.58 million in 2025 and will grow at a compound annual growth rate (CAGR) of 4.8% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 4.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2025 to 2033.
Orchid category is the fastest growing segment of the Preserved Flowers industry.

Market Dynamics of Preserved Flowers Market

Key Drivers for Preserved Flowers Market

Increasing Preferences of Consumers to Boost Market Growth

More and more contemporary buyers are looking for goods with long-lasting attractiveness, convenience, and distinctive visual appeal. Even though they are popular, traditional cut flowers have a short lifespan and need ongoing maintenance, which can be challenging. Preserved flowers, on the other hand, provide a durable substitute, maintaining their beauty and allure for extended periods of time—often years—without requiring particular attention or irrigation. People searching for memorable presents that last a lifetime and low-maintenance home décor will find this longevity appealing. Furthermore, because preserved flowers are so aesthetically pleasing, they may be used in a variety of contexts, from corporate gifts and home décor to bridal bouquets and upscale floral arrangements. Furthermore, the rise of the worldwide preserved flowers market has been driven by the spike in consumer expenditure on flower gifts during holiday seasons like Christmas and Halloween.

Increasing Advancements in Preservation Technologies to Boosts the Need for Advanced Preserved Flowers to Boost Market Growth

Improvements in technology has greatly increased the variety and quality of preserved flowers. Glycerinization and freeze-drying are two contemporary preservation methods that have improved the longevity and aesthetics of preserved flowers. In order to replenish the natural sap and preserve the flowers' suppleness and smoothness, glycerinization entails soaking them in a glycerin-water solution. Conversely, freeze-drying produces long-lasting, realistic blooms by eliminating moisture from flowers while maintaining their color and structure. The variety of flowers that can now be preserved has increased because to these advancements, including delicate and exotic types that was previously difficult to care for. The market is anticipated to see significantly more varied and superior products as preservation methods advance, satisfying a wider range of consumer tastes.

Restraint Factor for the Preserved Flowers Market

High Production Costs of Preserved Flowers, Will Limit Market Growth

The cost of producing preserved flowers is frequently higher than that of fresh flowers. Advanced methods including glycerinization, freeze-drying, and chemical treatments are used in flower preservation, and each one calls for certain tools and knowledge. Because these techniques can be costly, preserved flower products may cost more. The market may be restricted to higher-income clients due to the high cost of production, which also influences pricing methods. This may impede further market acceptance, particularly in areas or market segments where prices are crucial. The whole cost is further increased by the cost of quality control and upholding a constant standard of preservation, which may be prohibitive for startups or smaller busines...

This dataset tracks annual total students amount from 1987 to 2023 for Flowers Elementary School

Detection Of Flowers

## Overview

Detection Of Flowers is a dataset for object detection tasks - it contains Flowers annotations for 889 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).