metadata

annotations_creators: []
language: en
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
task_ids: []
pretty_name: StanfordDogsImbalanced
tags:
  - fiftyone
  - image
  - image-classification
dataset_summary: >



  ![image/png](dataset_preview.jpg)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 19060
  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("Voxel51/Stanford-Dogs-Imbalanced")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

Dataset Card for StanfordDogsImbalanced

This is a FiftyOne dataset with 19060 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("Voxel51/Stanford-Dogs-Imbalanced")

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

Dataset Details

Dataset Description

An imbalanced version of the Stanford Dogs dataset designed for testing class imbalance mitigation techniques, including but not limited to synthetic data generation.

This version of the dataset was constructed by randomly splitting the original dataset into train, val, and test sets with a 60/20/20 split. For 15 randomly chosen classes, we then removed all but 10 of the training examples.

# Split the dataset into train, val, and test sets
import fiftyone.utils.random as four
train, val, test = four.random_split(dataset, split_fracs=(0.6, 0.2, 0.2))
splits_dict = { "train": train, "val": val, "test": test }

# Get the classes to limit
import random
classes = list(dataset.distinct("ground_truth.label"))
classes_to_limit = random.sample(classes, 15)

# Limit the number of samples for the selected classes
for class_name in classes_to_limit:
    class_samples = dataset.match(F("ground_truth.label") == class_name)
    samples_to_keep = class_samples.take(10)
    samples_to_remove = class_samples.exclude(samples_to_keep)
    dataset.delete_samples(samples_to_remove)

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

Dataset Sources

Paper: [More Information Needed]
Homepage: [More Information Needed]

Uses

Fine-grained visual classification
Class imbalance mitigation strategies

Dataset Structure

The following classes only have 10 samples in the train split:

Australian_terrier
Saluki
Cardigan
standard_schnauzer
Eskimo_dog
American_Staffordshire_terrier
Lakeland_terrier
Lhasa
cocker_spaniel
Greater_Swiss_Mountain_dog
basenji
toy_terrier
Chihuahua
Walker_hound
Shih-Tzu
Newfoundland

Citation

BibTeX:

@inproceedings{KhoslaYaoJayadevaprakashFeiFei_FGVC2011,
  author = "Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei",
  title = "Novel Dataset for Fine-Grained Image Categorization",
  booktitle = "First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition",
  2011,
  month = "June",
  address = "Colorado Springs, CO",
}

Dataset Card Author

Jacob Marks

Dataset Contacts

[email protected] and [email protected]