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Image-text pairs from MS COCO2017.
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.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
})
})
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
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)
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