coco2017

Image-text pairs from MS COCO2017.

Data origin

• Data originates from cocodataset.org
• 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

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

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

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["file_name"])
    return example

dataset = load_dataset("phiyodr/coco2017")
dataset = dataset.map(create_full_path)