OxfordFlowers102 / README.md
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metadata
annotations_creators: []
language: en
license: other
size_categories:
  - 1K<n<10K
task_categories:
  - image-classification
  - image-segmentation
task_ids: []
pretty_name: Oxford Flowers 102
tags:
  - fiftyone
  - image
  - image-classification
  - image-segmentation
dataset_summary: >



  ![image/png](dataset_preview.gif)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8189
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  import fiftyone.utils.huggingface as fouh


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = fouh.load_from_hub("jamarks/OxfordFlowers102")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

Dataset Card for Oxford Flowers 102

image/png

This is a FiftyOne dataset with 8189 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("jamarks/OxfordFlowers102")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

  • Curated by: [More Information Needed]
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Language(s) (NLP): en
  • License: other

Dataset Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers?

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Annotations [optional]

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

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