---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: caption
    list: string
  - name: sentids
    list: string
  - name: split
    dtype: string
  - name: img_id
    dtype: string
  - name: filename
    dtype: string
  splits:
  - name: train
    num_bytes: 4044387988
    num_examples: 29000
  - name: test
    num_bytes: 142155397
    num_examples: 1000
  - name: validation
    num_bytes: 140557396.192
    num_examples: 1014
  download_size: 4306311970
  dataset_size: 4327100781.192
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
task_categories:
- text-generation
- image-to-text
- text-to-image
language:
- pt
pretty_name: Flickr30K Portuguese Translated
size_categories:
- 10K<n<100K
---

# 🎉 Flickr30K Portuguese Translated

## 💾 Dataset Summary

Flickr30K Portuguese Translated consists of 31,014 images, each accompanied by five descriptive captions that have been
generated by human annotators for every individual image. The original English captions were rendered into Portuguese
through the utilization of the Google Translator API.

The dataset is one of the results of work available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr.

## 🧑‍💻 Hot to Get Started with the Dataset

```python
from datasets import load_dataset

dataset = load_dataset('laicsiifes/flickr30k-pt-br')
```

## ✍️ Languages

The images descriptions in the dataset are in Portuguese.

## 🧱 Dataset Structure

### 📝 Data Instances

An example looks like below: 

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333>,
  'caption':
  [
    'Um cachorro preto carrega um brinquedo verde na boca enquanto caminha pela grama.',
    'Um cachorro preto molhado carrega um brinquedo verde pela grama.',
    'Um cachorro preto carregando algo pela grama.',
    'Um cachorro na grama com um item azul na boca.',
    'Um cachorro preto tem um brinquedo azul na boca.'
  ],
  'sentids': ['450', '451', '452', '453', '454'],
  'split': 'train',
  'img_id': '90',
  'filename': '1026685415.jpg'
}
```

### 🗃️ Data Fields

The data instances have the following fields:

- `image`: a `PIL.Image.Image` object containing the image.
- `caption`: a `list` of `str` containing the 5 captions related to the image.
- `sentids`: a `list` of `str` containing the 5 ordered identification numbers related to each caption.
- `split`: a `str` containing the data split. It stores the texts: `train`, `val` or `test`.
- `img_id`: a `str` containing the image identification number.
- `filename`: a `str` containing the name of the image file.

### ✂️ Data Splits

The dataset is partitioned using the Karpathy splitting appoach for Image Captioning
([Karpathy and Fei-Fei, 2015](https://arxiv.org/pdf/1412.2306)).

|Split|Samples|Average Caption Length (Words)|
|:-----------:|:-----:|:--------:|
|Train|29,000|12.1 ± 5.1|
|Validation|1,014|12.3 ± 5.3|
|Test|1,000|12.2 ± 5.4|
|Total|31,014|12.1 ± 5.2|


## 📋 BibTeX entry and citation info

```bibtex
@inproceedings{bromonschenkel2024comparative,
                title = "A Comparative Evaluation of Transformer-Based Vision 
                         Encoder-Decoder Models for Brazilian Portuguese Image Captioning",
               author = "Bromonschenkel, Gabriel and Oliveira, Hil{\'a}rio and 
                         Paix{\~a}o, Thiago M.",
            booktitle = "Proceedings...",
         organization = "Conference on Graphics, Patterns and Images, 37. (SIBGRAPI)",
                 year = "2024",
}
```