#PraCegoVer is a multi-modal dataset containing images associated to Portuguese captions based on posts from Instagram.
The #PraCegoVer dataset is available upon request for non-commercial purposes only. It can be requested and downloaded on our data repository on Zenodo.
The dataset is composed of the main file dataset.json and a collection of compressed files containing the images. The file dataset.json comprehends a list of json objects with the attributes: user, filename, raw_caption, caption and date. These attributes represent, respectively, the anonymized user that made the post, the image file name, the raw caption, the clean caption, and the post date. Each instance in dataset.json is associated with exactly one image in the images directory whose filename is pointed by the attribute filename. The instances have the following structure:
- 18/Jan/2023 - Download
We release pracegover_400k.json which contains 403,337 examples from the original dataset.json after preprocessing and duplication removal. It is split into train, validation, and test with 242036, 80628, and 80673 examples, respectively.
Automatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce. Then, inspired by the PraCegoVer moviment, we introduce the #PraCegoVer dataset.
#PraCegoVer has more than 500 thousand pairs with images and captions described in Portuguese collected from more than 14 thousand different profiles. Also, the mean caption length in #PraCegoVer is 39.3 words and standard deviation is 29.7, whereas the captions in MS COCO Captions 10.5 words on average and standard deviation of 2.2. These chacteristics make our dataset more challenging than MS COCO.
In Figure 1, we show the growth of #PraCegoVer dataset compared to the number of total posts tagging #PraCegoVer on Instagram over time. Currently, #PraCegoVer dataset contains about 30% of all public posts tagging #PraCegoVer.
Figure 1: The total number of posts tagging #PraCegoVer (dashed line) and #PraCegoVer dataset size (solid line) throughout the time.
After a thorough exploratory analysis, we extracted some relevant topics from the captions in #PraCegoVer dataset, as illustrated in Figure 2. We also found well-formed clusters that represent images of advertisements, birds and airplanes (Figures 3, 4 and 5, respectively).
Figure 2: Relevant topics found within the captions in #PraCegoVer dataset.
Figure 3: Images sampled from a cluster with advertisements.
Figure 4: Images sampled from a cluster with birds.
Figure 5: Images sampled from a cluster with airplanes.
Figure 6 shows the distribution of captions by length in #PraCegoVer and MS COCO Captions datasets. #PraCegoVer's captions have on average 40 words, while MS COCO Captions has only about 10. Also, it can be seen that the variance in #PraCegoVer is considerably higher than in MS COCO.
Figure 6: Histogram illustrating the distribuition of caption length in terms of number of words.
Figure 7 shows the distribution of words by frequency, i.e., the number of occurrences of that word, for #PraCegoVer and MS COCO Captions. On the x-axis, we show ranges of word frequency, and on the y-axis, we show the number of words whose frequency is within that band. #PraCegoVer has by far more words occurring five or fewer times within the captions than MS COCO.
Figure 7: Histogram illustrating the distribuition of words with respect to the frequency
These characteristics make #PraCegoVer more challenging than MS COCO Captions.
Gabriel Oliveira (@gabrielsantosrv), Esther Colombini (@estherlc) and Sandra Avila (@sandraavila).
If you use our code or dataset, please cite as:
@Article{pracegover2022,
AUTHOR = {dos Santos, Gabriel Oliveira and Colombini, Esther Luna and Avila, Sandra},
TITLE = {#PraCegoVer: A Large Dataset for Image Captioning in Portuguese},
JOURNAL = {Data},
VOLUME = {7},
YEAR = {2022},
NUMBER = {2},
ARTICLE-NUMBER = {13},
URL = {https://www.mdpi.com/2306-5729/7/2/13},
ISSN = {2306-5729},
DOI = {10.3390/data7020013}
}