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Food Recognition 2022 Dataset

439624982395
Tagfood
Tasksemantic segmentation
Release YearMade in 2022
LicenseCC0 1.0
Download3 GB

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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Dataset LinkHomepage

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.

OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image from Food Recognition 2022
OpenSample annotation mask from Food Recognition 2022Sample image 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.

Search
Rows 1-10 of 498
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
waterβž”
polygon
3002
5414
1.8
23.66%
salad-leaf-salad-greenβž”
polygon
2064
2385
1.16
32.92%
bread-whiteβž”
polygon
1930
2265
1.17
30.27%
tomato-rawβž”
polygon
1925
3352
1.74
11.28%
butterβž”
polygon
1642
2441
1.49
11.61%
carrot-rawβž”
polygon
1509
2220
1.47
13.28%
bread-wholemealβž”
polygon
1488
1708
1.15
27.04%
coffee-with-caffeineβž”
polygon
1433
2035
1.42
13.46%
riceβž”
polygon
1048
1204
1.15
21.03%
eggβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 95009
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
bread
polygon
145958.jpg
972 x 973
683px
70.27%
898px
92.29%
34%
2βž”
tomato-raw
polygon
145958.jpg
972 x 973
634px
65.23%
806px
82.84%
28.1%
3βž”
hummus
polygon
145958.jpg
972 x 973
257px
26.44%
385px
39.57%
3.5%
4βž”
hummus
polygon
145958.jpg
972 x 973
256px
26.34%
242px
24.87%
2.64%
5βž”
onion
polygon
145958.jpg
972 x 973
341px
35.08%
483px
49.64%
9.65%
6βž”
onion
polygon
145958.jpg
972 x 973
325px
33.44%
420px
43.17%
5.64%
7βž”
onion
polygon
145958.jpg
972 x 973
239px
24.59%
218px
22.4%
4.1%
8βž”
fish-fingers-breaded
polygon
126651.jpg
464 x 464
452px
97.41%
90px
19.4%
15.38%
9βž”
salmon
polygon
131323.jpg
972 x 973
949px
97.63%
966px
99.28%
69.65%
10βž”
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 = { mar },
  note = { visited on 2024-03-05 },
}

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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