# coco2017

Image-text pairs from [MS COCO2017](https://cocodataset.org/#download).

## Data origin

* Data originates from [cocodataset.org](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)
* While `coco-karpathy` uses a dense format (with several sentences and sendids per row), `coco-karpathy-long` uses a long format with one `sentence` (aka caption) and `sendid` per row. `coco-karpathy-long` uses the first five sentences and therefore is five times as long as `coco-karpathy`.
    * `phiyodr/coco2017`: One row corresponds one image with several sentences.
    * `phiyodr/coco2017-long`: One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details.

## Format

```python
DatasetDict({
    train: Dataset({
        features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
        num_rows: 118287
    })
    validation: Dataset({
        features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
        num_rows: 5000
    })
})
```

## Usage

* Download image data and unzip
  
```bash
cd PATH_TO_IMAGE_FOLDER

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
#wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip # zip not needed: everything you need is in load_dataset("phiyodr/coco2017")

unzip train2017.zip
unzip val2017.zip
```

* Load dataset in Python
  
```python
import os
from datasets import load_dataset
PATH_TO_IMAGE_FOLDER = "COCO2017"

def create_full_path(example):
    """Create full path to image using `base_path` to COCO2017 folder."""
    example["image_path"] = os.path.join(PATH_TO_IMAGE_FOLDER, example["filepath"], example["filename"])
    return example

dataset = load_dataset("phiyodr/coco2017")
dataset = dataset.map(create_full_path)
```
task_categories:image-to-text task_ids:image-captioning size_categories:100K<n<1M language:en coco image-captioning region:us
{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "task_ids": ["image-captioning"], "pretty_name": "COCO2017", "tags": ["coco", "image-captioning"], "dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "image_id", "dtype": "int64"}, {"name": "ids", "sequence": "int64"}, {"name": "captions", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 64026361, "num_examples": 118287}, {"name": "validation", "num_bytes": 2684731, "num_examples": 5000}], "download_size": 30170127, "dataset_size": 66711092}}
TAGS
#task_categories-image-to-text #task_ids-image-captioning #size_categories-100K<n<1M #language-English #coco #image-captioning #region-us 

# coco2017

Image-text pairs from MS COCO2017.

## Data origin

* Data originates from URL
* While 'coco-karpathy' uses a dense format (with several sentences and sendids per row), 'coco-karpathy-long' uses a long format with one 'sentence' (aka caption) and 'sendid' per row. 'coco-karpathy-long' uses the first five sentences and therefore is five times as long as 'coco-karpathy'.
    * 'phiyodr/coco2017': One row corresponds one image with several sentences.
    * 'phiyodr/coco2017-long': One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details.

## Format



## Usage

* Download image data and unzip
  


* Load dataset in Python
  

# coco2017

Image-text pairs from MS COCO2017. ## Data origin

* Data originates from URL
* While 'coco-karpathy' uses a dense format (with several sentences and sendids per row), 'coco-karpathy-long' uses a long format with one 'sentence' (aka caption) and 'sendid' per row. 'coco-karpathy-long' uses the first five sentences and therefore is five times as long as 'coco-karpathy'.
    * 'phiyodr/coco2017': One row corresponds one image with several sentences.
    * 'phiyodr/coco2017-long': One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details. ## Format ## Usage

* Download image data and unzip
  


* Load dataset in Python
TAGS
#task_categories-image-to-text #task_ids-image-captioning #size_categories-100K<n<1M #language-English #coco #image-captioning #region-us 
 # coco2017

Image-text pairs from MS COCO2017. ## Data origin

* Data originates from URL
* While 'coco-karpathy' uses a dense format (with several sentences and sendids per row), 'coco-karpathy-long' uses a long format with one 'sentence' (aka caption) and 'sendid' per row. 'coco-karpathy-long' uses the first five sentences and therefore is five times as long as 'coco-karpathy'.
    * 'phiyodr/coco2017': One row corresponds one image with several sentences.
    * 'phiyodr/coco2017-long': One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details. ## Format ## Usage

* Download image data and unzip
  


* Load dataset in Python
passage: TAGS
#task_categories-image-to-text #task_ids-image-captioning #size_categories-100K<n<1M #language-English #coco #image-captioning #region-us 
# coco2017

Image-text pairs from MS COCO2017.## Data origin

* Data originates from URL
* While 'coco-karpathy' uses a dense format (with several sentences and sendids per row), 'coco-karpathy-long' uses a long format with one 'sentence' (aka caption) and 'sendid' per row. 'coco-karpathy-long' uses the first five sentences and therefore is five times as long as 'coco-karpathy'.
    * 'phiyodr/coco2017': One row corresponds one image with several sentences.
    * 'phiyodr/coco2017-long': One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details.## Format## Usage

* Download image data and unzip
  


* Load dataset in Python