metadata

annotations_creators: []
language: en
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
task_ids: []
pretty_name: Food101
tags:
  - fiftyone
  - image
  - image-classification
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000
  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/Food101")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

Dataset Card for Food-101

This is a FiftyOne dataset with 35000 samples.

Note: This dataset is subset of the full Food101 dataset. The recipe notebook for creating this dataset can be found here

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/Food101")

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

Dataset Details

Dataset Description

The Food-101 dataset is a large-scale dataset for food recognition, consisting of 101,000 images across 101 different food categories.

Here are the key details:

Contains a total of 101,000 images
Each food class has 1,000 images, with 750 training images and 250 test images per class
All images were rescaled to have a maximum side length of 512 pixels
Curated by: Lukas Bossard, Matthieu Guillaumin, Luc Van Gool
Funded by: Computer Vision Lab, ETH Zurich, Switzerland
Shared by: Harpreet Sahota, Hacker-in-Residence at Voxel51
Language(s) (NLP): en
License: The dataset images come from Foodspotting and are not owned by the creators of the Food-101 dataset (ETH Zurich). Any use beyond scientific fair use must be negotiated with the respective picture owners according to the Foodspotting terms of use

Dataset Sources

Repository: https://huggingface.co/datasets/ethz/food101
Website: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/
Paper: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf

Citation

BibTeX:

@inproceedings{bossard14,
  title = {Food-101 -- Mining Discriminative Components with Random Forests},
  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
  booktitle = {European Conference on Computer Vision},
  year = {2014}
}