randall-lab/food101

Dataset Card for Food-101

Dataset Details

Dataset Description

This is a dataset of 101 food categories, with 101,000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

Dataset Sources

• Homepage: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/
• Paper: Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food-101–mining discriminative components with random forests. In Computer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part VI 13 (pp. 446-461). Springer International Publishing.

Dataset Structure

Total images: 101,000

Classes: 101 food categories

Splits:

• Train: 75,750 images

• Test: 25,250 images

Image specs: JPG format, variable resolution (maximum side length of 512), RGB

Example Usage

Below is a quick example of how to load this dataset via the Hugging Face Datasets library.

from datasets import load_dataset  

# Load the dataset  
dataset = load_dataset("randall-lab/food101", split="train", trust_remote_code=True)   
# dataset = load_dataset("randall-lab/food101", split="test", trust_remote_code=True)  

# Access a sample from the dataset  
example = dataset[0]  
image = example["image"]  
label = example["label"]  

image.show()  # Display the image  
print(f"Label: {label}")

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} }