Datasets:
Tasks:
Image-to-Text
Sub-tasks:
image-captioning
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
File size: 3,517 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""SBU Captioned Photo Dataset"""
import json
import datasets
_CITATION = """\
@inproceedings{NIPS2011_5dd9db5e,
author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara},
booktitle = {Advances in Neural Information Processing Systems},
editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Im2Text: Describing Images Using 1 Million Captioned Photographs},
url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf},
volume = {24},
year = {2011}
}
"""
_DESCRIPTION = """\
The SBU Captioned Photo Dataset is a collection of over 1 million images with associated text descriptions extracted from Flicker.
"""
_LICENSE = "unknown"
_HOMEPAGE = "https://www.cs.rice.edu/~vo9/sbucaptions/"
_URL = "https://www.cs.rice.edu/~vo9/sbucaptions/sbu-captions-all.tar.gz"
_FEATURES = datasets.Features(
{"image_url": datasets.Value("string"), "user_id": datasets.Value("string"), "caption": datasets.Value("string")}
)
_MAP_SBU_FEATURES_TO_DATASETS_FEATURES = {"image_urls": "image_url", "user_ids": "user_id", "captions": "caption"}
class SBUCaptionedPhotoDatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for SBU Captioned Photo dataset."""
VERSION = datasets.Version("0.0.0")
def __init__(self, version=None, *args, **kwargs):
super().__init__(
version=version or self.VERSION,
*args,
**kwargs,
)
class SBUCaptionedPhotoDataset(datasets.GeneratorBasedBuilder):
"""SBU Captioned Photo dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
archive = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_archive(archive),
},
)
]
def _generate_examples(self, files):
annotations = None
for path, f in files:
if path.endswith("sbu-captions-all.json"):
annotations = json.loads(f.read().decode("utf-8"))
break
# Sanity checks
assert annotations is not None
nb_samples = len(annotations[next(iter(annotations.keys()))])
assert all(len(values) == nb_samples for values in annotations.values())
keys = tuple(annotations.keys())
for idx in range(nb_samples):
yield idx, {_MAP_SBU_FEATURES_TO_DATASETS_FEATURES[key]: annotations[key][idx] for key in keys}
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