A commercial product data set for multimodel machine translation
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IKEA Dataset

License: MIT


If you use this dataset, you might want to cite this paper:

  author        = {Mingyang Zhou and
                   Runxiang Cheng and
                   Yong Jae Lee and
                   Zhou Yu},
  title         = {A Visual Attention Grounding Neural Model for Multimodal Machine Translation},
  year          = {2018},
  url           = {https://arxiv.org/pdf/1808.08266.pdf},


IKEA dataset is a multilingual-multimodal dataset published along with this paper: A Visual Attention Grounding Neural Model for Multimodal Machine Translation. It contains two language pairs: English-French and English-German. The text data is language-corresponded descriptions of all products crawled from IKEA and UNDERAMOUR. For each data sample in each language pairs, there is a corresponding product image that is compressed into a feature vector of size 2048.

Data Preprocessing:

Besides the raw, unprocessed version of all the data samples, there are two other versions of the data. the IKEA/data.en.*/data.norm.tok.lc folder contains normalized, tokenized, converted to lowercase (processed exclusively in such order) data. The IKEA/data.en.*/data.norm.tok.lc.bpe folder contains normalized, tokenized, converted to lowercase, byte-pair encoding (processed exclusively in such order) data.




The below statistics is calculated with unprocessed data:

Language pair Language Tokens Minimum sample length Maximum sample length Average sample length Standard derivation sample length Vocabulary size
English-German English 256355 6 343 71.40807799 46.33073895 6601
German 216892 6 324 60.41559889 39.14467817 10468
English-French English 239966 6 334 72.25715146 47.24279926 6442
French 275251 6 469 82.88196326 54.72162651 7575

These four histogram show the sentence length distribution for each language in each languague pairs. The length of a sentence is calculate with the number tokens in the sentence:


  • Because all data samples are the description of different products from IKEA or UNDERAMOUR, a data sample usually contain more than one sentences.
  • A data sample is not a strict description of the corresponding product image. A description might contain information that cannot be showed in image. for example, a description for an Underamour product can contains the sentence “Don’t wash it with hot water”.
  • A data sample in German or French might be less complete with its corresponding English data sample because some part of the descriptions of certain products are not available in non-English regions.

Data Format:


  • IKEA/: data crawled and processed from IKEA and UNDERAMOUR.
  • IKEA/data.en.fr: English-French data.
  • IKEA/data.en.de: English-German data.
  • IKEA/data.en.*/data.raw: unprocessed original data compressed in .gz.
  • IKEA/data.en.*/data.norm.tok.lc: normalized, tokenized and lowercase-converted data.
  • IKEA/data.en.*/data.norm.tok.lc.bpe: normalized, tokenized, lowercase-converted, byte-pair-encoded (10000) data.
  • IKEA/data.en.*/data.image.bpe: image matrix for train.*, test.*, val.*.
  • IKEA/image/image.en.*: compressed images in jpg format for training, validation and testing.

Data Files:

  • train.*: 2600+ samples for FR, 2800+ samples for DE.
  • test.*: 330+ samples for FR, 360+ samples for DE.
  • val.*: 330+ samples for FR, 360+ samples for DE.
  • IKEA/image/image.en.*/*.[12].zip: each store half of the images for training, validation and testing.
  • vocab.*: language-corresponded vocabulary file extract from *.norm.tok.lc.10000bpe.*.
  • *_file.code: language files for byte-pair encoding.
  • *.norm.tok.lc.10000bpe_ims.npy: corresponded image matrix for train.*, test.*, val.*, each image is stored in a vector of size 2048.


It can be used for text-only neural machine translation project and multimodal machine translation project. To download the dataset, open the directory where you want to copy the data to on terminal, enter:

$ git clone https://github.com/sampalomad/IKEA-Dataset.git