Datasets:
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---
language:
- pt
size_categories:
- 100K<n<1M
task_categories:
- text-to-image
- image-to-text
- text-generation
pretty_name: COCO Captions Portuguese Translation
dataset_info:
features:
- name: image
dtype: image
- name: caption
sequence: string
- name: url
dtype: string
- name: filepath
dtype: string
- name: filename
dtype: string
- name: sentids
sequence: int64
- name: imgid
dtype: int64
- name: split
dtype: string
- name: cocoid
dtype: int64
splits:
- name: train
num_bytes: 4284853468.21
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: 1587879327.48
num_examples: 30504
download_size: 6358581380
dataset_size: 6390589447.690001
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 Translation for Portuguese Image Captioning
## 💾 Dataset Summary
COCO Captions Portuguese Translation, a multimodal dataset for Portuguese image captioning with 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 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, the `train` and `restval` splits
are put together as an unique training 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 Translation 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}
}
``` |