---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: filepath
    dtype: string
  - name: sentids
    list: int32
  - name: filename
    dtype: string
  - name: imgid
    dtype: int32
  - name: split
    dtype: string
  - name: sentences_tokens
    list:
      list: string
  - name: sentences_raw
    list: string
  - name: sentences_sentid
    list: int32
  - name: cocoid
    dtype: int32
  - name: th_sentences_raw
    sequence: string
  splits:
  - name: test
    num_bytes: 819234726.0
    num_examples: 5000
  - name: validation
    num_bytes: 807387321.0
    num_examples: 5000
  - name: train
    num_bytes: 18882795327.165
    num_examples: 113287
  download_size: 20158273111
  dataset_size: 20509417374.165
---

## Usage

```python
from datasets import load_dataset
dataset = load_dataset("patomp/thai-mscoco-2014-captions")
dataset
```
output
```python
DatasetDict({
    train: Dataset({
        features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
        num_rows: 113287
    })
    validation: Dataset({
        features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
        num_rows: 5000
    })
    test: Dataset({
        features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
        num_rows: 5000
    })
})
```

A sample
```python
dataset["validation"][0]
```
output
```python
{
   "image":<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x7F6C5A83F430>,
   "filepath":"COCO_val2014_000000184613.jpg",
   "sentids":[474921,479322,479334,481560,483594],
   "filename":"COCO_val2014_000000184613.jpg",
   "imgid":2,
   "split":"val",
   "sentences_tokens":[
      ["a", "child","holding", "a","flowered","umbrella","and","petting","a","yak"],["a","young","man","holding","an","umbrella","next","to","a","herd","of","cattle"],
      ["a","young","boy","barefoot","holding","an","umbrella","touching","the","horn","of","a","cow"],
      ["a","young","boy","with","an","umbrella","who","is","touching","the","horn","of","a","cow"],
      ["a","boy","holding","an","umbrella","while","standing","next","to","livestock"]
   ],
   "sentences_raw":[
      "A child holding a flowered umbrella and petting a yak.",
      "A young man holding an umbrella next to a herd of cattle.",
      "a young boy barefoot holding an umbrella touching the horn of a cow",
      "A young boy with an umbrella who is touching the horn of a cow.",
      "A boy holding an umbrella while standing next to livestock."
   ],
   "sentences_sentid":[474921,479322,479334,481560,483594],
   "cocoid":184613,
   "th_sentences_raw":[
      "เด็กถือร่มที่มีดอกหนึ่งคันและลูบคลูบลํา",
      "ชายหนุ่มคนหนึ่งถือร่มไว้ข้างๆ ฝูงวัว",
      "เด็กหนุ่มคนหนึ่งเท้าเปล่าจับร่มจับแตรของวัว",
      "เด็กชายที่มีร่มสัมผัสแตรของวัว",
      "เด็กชายถือร่มในขณะที่ยืนถัดจากปศุสัตว์"
   ]
}
```

## Dataset Construction

The dataset contructed from translating the captions of [MS COCO 2014 dataset](https://huggingface.co/datasets/HuggingFaceM4/COCO) [1] to Thai by using [NMT](https://airesearch.in.th/releases/machine-translation-models/) provided by VISTEC-depa Thailand Artificial Intelligence Research Institute [2]. The translated of 3 splits (train, validation and test) dataset was published in the [Huggingface](https://huggingface.co/datasets/patomp/thai-mscoco-2014-captions).

## References

[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision – ECCV 2014, Springer International Publishing, Cham, 740–755. 

[2] English-Thai Machine Translation Models. (2020, June 23).  VISTEC-depa Thailand Artificial Intelligence Research Institute. https://airesearch.in.th/releases/machine-translation-models/