
## 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/
region:us
{"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}}
TAGS
#region-us 


## Usage


output


A sample

output


## Dataset Construction

The dataset contructed from translating the captions of MS COCO 2014 dataset [1] to Thai by using NMT 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.

## 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. URL
## Usage


output


A sample

output ## Dataset Construction

The dataset contructed from translating the captions of MS COCO 2014 dataset [1] to Thai by using NMT 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. ## 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. URL
TAGS
#region-us 
 ## Usage


output


A sample

output ## Dataset Construction

The dataset contructed from translating the captions of MS COCO 2014 dataset [1] to Thai by using NMT 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. ## 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. URL
passage: TAGS
#region-us 
## Usage


output


A sample

output## Dataset Construction

The dataset contructed from translating the captions of MS COCO 2014 dataset [1] to Thai by using NMT 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.## 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. URL