---
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

<!-- Provide a quick summary of the dataset. -->




![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 Details

### Dataset Description

An imbalanced version of the [Stanford Dogs dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) 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.

```python
# 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

<!-- Provide the basic links for the dataset. -->

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

## Uses

- Fine-grained visual classification
- Class imbalance mitigation strategies

<!-- Address questions around how the dataset is intended to be used. -->


## 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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->


## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```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](https://huggingface.co/jamarks)

## Dataset Contacts

aditya86@cs.stanford.edu and bangpeng@cs.stanford.edu