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.

The Oxford 102 Category Flower dataset consists of 8,189 images with 102 categories of flowers which commonly occurs in the United Kingdom, and each category has 40 to 258 images.

Overview

We have created a 102 category dataset, consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images. The details of the categories and the number of images for each class can be found on this category statistics page.

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. The dataset is visualized using isomap with shape and colour features.

Directory Structure

> dataset
  > train
  > valid
  > test
- cat_to_name.json
- README.md
- sample_submission.csv

Visualization of the dataset

We visualize the categories in the dataset using SIFT features as shape descriptors and HSV as colour descriptor. The images are randomly sampled from the category.

https://i.imgur.com/Tl6TKUC.png" alt="">

Publications

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)

Source

• Original source of this data can be found in 102 Category Flower Dataset
• Original readme from author can be found in AUTHOR README
• Directory test is added from another kaggle dataset that can be found in Oxford 102 Flower Pytorch

Oxford 102 flower dataset is a 102 category dataset, consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images. The details of the categories and the number of images for each class can be found on this category statistics page.

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. The dataset is visualized using isomap with shape and colour features.

The Oxford Flowers 102 dataset is a consistent of 102 flower categories 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.

The dataset is divided into a training set, a validation set and a test set. The training set and validation set each consist of 10 images per class (totalling 1020 images each). The test set consists of the remaining 6149 images (minimum 20 per class).

Note: The dataset by default comes with a test size larger than the train size. For more info see this issue.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('oxford_flowers102', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/oxford_flowers102-2.1.1.png" alt="Visualization" width="500px">

A 102 category dataset consisting of 102 flower categories, commonly occuring in the United Kingdom. Each class consists of 40 to 258 images. The images have large scale, pose and light variations.

"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."