README.md

---
language:
- pt
size_categories:
- 100K<n<1M
task_categories:
- text-to-image
- image-to-text
- text-generation
pretty_name: COCO Captions Portuguese Translated
dataset_info:
  features:
  - name: filepath
    dtype: string
  - name: sentids
    sequence: int64
  - name: filename
    dtype: string
  - name: imgid
    dtype: int64
  - name: split
    dtype: string
  - name: caption
    sequence: string
  - name: cocoid
    dtype: int64
  - name: url
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 4284853466.125
    num_examples: 82783
  - name: test
    num_bytes: 258794470.0
    num_examples: 5000
  - name: validation
    num_bytes: 259062182.0
    num_examples: 5000
  - name: restval
    num_bytes: 1587879325.0
    num_examples: 30504
  download_size: 6358579460
  dataset_size: 6390589443.125
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
  - split: restval
    path: data/restval-*
---


# 🎉 COCO Captions Dataset Translated for Portuguese Image Captioning

## 💾 Dataset Summary

COCO Captions Portuguese Translated consists of 123,287 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.

## 🧑‍💻 Hot to Get Started with the Dataset

```python
from datasets import load_dataset

dataset = load_dataset('laicsiifes/coco-captions-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=640x480>,
  'caption': [
    'Um restaurante possui mesas e cadeiras modernas de madeira.',
    'Uma longa mesa de restaurante com cadeiras de vime com encosto arredondado.',
    'uma longa mesa com uma planta em cima cercada por cadeiras de madeira',
    'Uma longa mesa com um arranjo de flores no meio para reuniões',
    'Uma mesa é adornada com cadeiras de madeira com detalhes em azul.'
  ],
  'url': 'http://images.cocodataset.org/train2014/COCO_train2014_000000057870.jpg',
  'filepath': 'train2014',
  'filename': 'COCO_train2014_000000057870.jpg',
  'sentids': [787980, 789366, 789888, 791316, 794853],
  'imgid': 40504,
  'split': 'train',
  'cocoid': 57870
}
```

### 🗃️ Data Fields

The data instances have the following fields:

- `image`: a `PIL.Image.Image` object containing image.
- `caption`: a `list` of `str` containing the 5 captions related to image.
- `url`: a `str` containing the url to original image.
- `filepath`: a `str` containing the path to image file.
- `filename`: a `str` containing name of image file.
- `sentids`: a `list` of `int` containing the 5 ordered identification numbers related to each caption.
- `imgid`: a `int` containing image identification number.
- `split`: a `str` containing data split. It stores texts: `train`, `val`, `restval` or `test`.
- `cocoid`: an `int` containing example identifier in COCO dataset.

### ✂️ 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)). For training, train and restval splits
are put together as an unique train split with 113,287 examples.

|Split|Samples|Average Caption Length (Words)|
|:-----------:|:-----:|:--------:|
|Train|82,783|10.3 ± 2.7|
|RestVal|30,504|10.3 ± 2.7|
|Validation|5,000|10.3 ± 2.7|
|Test|5,000|10.3 ± 2.7|
|Total|123,287|10.3 ± 2.7|


## 📋 BibTeX entry and citation info

```bibtex
@misc{bromonschenkel2024cocopt,
  title        = "COCO Captions Dataset Translated for Portuguese Image Captioning",
  author       = "Bromonschenkel, Gabriel and Oliveira, Hil{\'a}rio and Paix{\~a}o, Thiago M.",
  howpublished = "\url{https://huggingface.co/datasets/laicsiifes/coco-captions-pt-br}",
  publisher    = {Hugging Face},
  year         = "2024"
}
```