Food Recognition 2022 - Dataset Ninja

Introduction #

The authors of the Food Recognition 2022 dataset emphasize the significance of image-based food recognition, which holds utility across various applications. Particularly, this recognition capability offers a streamlined method for individuals to monitor their dietary intake by capturing images of consumed food. Such tracking has both personal and medical implications, as it assists in studies focusing on participants’ food intake, a task that has previously relied on imprecise food frequency questionnaires.

The advancement of deep learning has significantly propelled image-based food recognition in recent years. However, the complex nature of food recognition persists due to numerous challenges. This benchmark, now in its third consecutive year on AIcrowd, continues to build upon the achievements of the previous Food Recognition Challenges held in 2019, 2020, and 2021.

The primary objective of this benchmark is to train models capable of examining images of food items and identifying the individual components within them. To facilitate this, a novel dataset of food images has been curated using the MyFoodRepo app. This app involves Swiss volunteers who contribute images of their daily food consumption as part of the digital cohort named Food & You.

This evolving dataset has been meticulously annotated, or its automatic annotations have been validated, with respect to segmentation, classification (mapping individual food items to a Swiss Food ontology), and estimations of weight/volume. As the dataset continues to expand, additional data will be periodically released.

Locating annotated food images poses challenges, as most existing databases possess limited annotations. Notably, the majority of food images available online are visually appealing but often diverge from real-world representations. Algorithms require real-world images, and proper annotations are generally lacking. Ideally, food images should be accompanied by accurate segmentation, classification, and volume/weight estimates.

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Summary #

Food Recognition 2022 v2.0 is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the food and medical industries.

The dataset consists of 43962 images with 95009 labeled objects belonging to 498 different classes including water, salad-leaf-salad-green, bread-white, and other: tomato-raw, butter, carrot-raw, bread-wholemeal, coffee-with-caffeine, rice, egg, apple, mixed-vegetables, jam, cucumber, wine-red, cheese, banana, potatoes-steamed, bell-pepper-red-raw, espresso-with-caffeine, hard-cheese, bread-whole-wheat, tea, mixed-salad-chopped-without-sauce, avocado, white-coffee-with-caffeine, tomato-sauce, wine-white, and 470 more.

Images in the Food Recognition 2022 dataset have pixel-level semantic segmentation annotations. There are 3001 (7% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: training (39962 images), test (3000 images), and validation (1000 images). The dataset was released in 2022 by the AIcrowd, Switzerland.

Here is a visualized example for randomly selected sample classes:

Explore #

Food Recognition 2022 dataset has 43962 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Food Recognition 2022

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Have a look at 43962 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 498 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-10 of 498

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
water➔ polygon	3002	5414	1.8	23.66%
salad-leaf-salad-green➔ polygon	2064	2385	1.16	32.92%
bread-white➔ polygon	1930	2265	1.17	30.27%
tomato-raw➔ polygon	1925	3352	1.74	11.28%
butter➔ polygon	1642	2441	1.49	11.61%
carrot-raw➔ polygon	1509	2220	1.47	13.28%
bread-wholemeal➔ polygon	1488	1708	1.15	27.04%
coffee-with-caffeine➔ polygon	1433	2035	1.42	13.46%
rice➔ polygon	1048	1204	1.15	21.03%
egg➔ polygon	1035	1279	1.24	17.31%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-10 of 498

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
water polygon	5414	23.49%	81.81%	0%	2px	0.35%	1896px	98.54%	363px	56.73%	2px	0.42%	2717px	98.75%
tomato-raw polygon	3352	6.63%	81.06%	0%	2px	0.21%	1997px	99.34%	173px	26.7%	3px	0.32%	1896px	99.78%
butter polygon	2441	8.03%	67.56%	0%	3px	0.35%	2189px	99.38%	202px	31.81%	3px	0.47%	2803px	98.5%
salad-leaf-salad-green polygon	2385	28.71%	92.08%	0%	2px	0.42%	3186px	99.75%	382px	59.12%	3px	0.48%	2979px	100%
bread-white polygon	2265	27.01%	82.62%	0%	5px	0.64%	3509px	99.17%	365px	56.89%	4px	0.43%	2976px	99.73%
carrot-raw polygon	2220	9.01%	86.9%	0%	2px	0.42%	3136px	98.77%	222px	34.08%	2px	0.42%	2934px	98.97%
coffee-with-caffeine polygon	2035	13.64%	87.39%	0%	2px	0.42%	1467px	98%	205px	33.1%	2px	0.32%	1967px	98%
bread-wholemeal polygon	1708	24.2%	88.22%	0%	4px	0.39%	1442px	99.3%	359px	53.74%	4px	0.39%	1513px	99.58%
egg polygon	1279	14.6%	80.46%	0.01%	9px	1.14%	1599px	99%	256px	40.88%	8px	0.9%	1271px	99%
rice polygon	1204	18.37%	87.37%	0%	5px	0.88%	1966px	99.38%	313px	47.79%	6px	0.59%	2208px	99.25%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 95009 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 95009

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	bread polygon	145958.jpg	972 x 973	683px	70.27%	898px	92.29%	34%
2➔	tomato-raw polygon	145958.jpg	972 x 973	634px	65.23%	806px	82.84%	28.1%
3➔	hummus polygon	145958.jpg	972 x 973	257px	26.44%	385px	39.57%	3.5%
4➔	hummus polygon	145958.jpg	972 x 973	256px	26.34%	242px	24.87%	2.64%
5➔	onion polygon	145958.jpg	972 x 973	341px	35.08%	483px	49.64%	9.65%
6➔	onion polygon	145958.jpg	972 x 973	325px	33.44%	420px	43.17%	5.64%
7➔	onion polygon	145958.jpg	972 x 973	239px	24.59%	218px	22.4%	4.1%
8➔	fish-fingers-breaded polygon	126651.jpg	464 x 464	452px	97.41%	90px	19.4%	15.38%
9➔	salmon polygon	131323.jpg	972 x 973	949px	97.63%	966px	99.28%	69.65%
10➔	risotto-without-cheese-cooked polygon	177552.jpg	840 x 840	500px	59.52%	470px	55.95%	20.93%

License #

Food Recognition 2022 is under CC0 1.0 license.

Source

Citation #

If you make use of the Food Recognition 2022 data, please cite the following reference:

@dataset{Food Recognition 2022,
	author={AIcrowd},
	title={Food Recognition 2022},
	year={2022},
	url={https://www.kaggle.com/datasets/awsaf49/food-recognition-2022-dataset}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-food-recognition-dataset,
  title = { Visualization Tools for Food Recognition 2022 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/food-recognition } },
  url = { https://datasetninja.com/food-recognition },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-16 },
}

Download #

Dataset Food Recognition 2022 can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Food Recognition 2022', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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