mlsanityData to 7 Oct 2026

FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptions

arXiv 2110.02035 ↗First posted 5 Oct 2021cs.CV cs.CL cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

In this paper we introduce the FooDI-ML dataset. This dataset contains over 1.5M unique images and over 9.5M store names, product names descriptions, and collection sections gathered from the Glovo application. The data made available corresponds to food, drinks and groceries products from 37 countries in Europe, the Middle East, Africa and Latin America. The dataset comprehends 33 languages, including 870K samples of languages of countries from Eastern Europe and Western Asia such as Ukrainian and Kazakh, which have been so far underrepresented in publicly available visio-linguistic datasets. The dataset also includes widely spoken languages such as Spanish and English. To assist further research, we include benchmarks over two tasks: text-image retrieval and conditional image generation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

22 of 40 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  6. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  7. 2016Bag of Tricks for Efficient Text Classificationcited by 869
  8. 2018Self-Attention Generative Adversarial Networkscited by 809
  9. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  10. 2021WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learningcited by 285
  11. 2015YFCC100M: The New Data in Multimedia Researchcited by 234
  12. 2019Billion-scale semi-supervised learning for image classificationcited by 232
10 more
  1. 2017Learning to Reason: End-to-End Module Networks for Visual Question Answeringcited by 125
  2. 2018Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Imagescited by 118
  3. 2016Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challengecited by 116
  4. 2019CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrievalcited by 110
  5. 2018COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrievalcited by 80
  6. 2020ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Networkcited by 47
  7. 2019FoodX-251: A Dataset for Fine-grained Food Classificationcited by 33
  8. 2017ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognitioncited by 21
  9. 2016Multimodal Pivots for Image Caption Translationcited by 18
  10. 2020UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioningcited by 13

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022Improving Commonsense in Vision-Language Models via Knowledge Graph Riddlescited by 7
  2. 2022GLAMI-1M: A Multilingual Image-Text Fashion Datasetcited by 3
  3. 2023Constructing Image-Text Pair Dataset from Bookscited by 3
  4. 2025Evaluating Perspectival Biases in Cross-Modal Retrievalcited by 0