Add SBU Captions Photo Dataset (#4130)

* Add SBU Captions

Co-authored-by: mariosasko <[email protected]>

Commit from https://github.com/huggingface/datasets/commit/01c7f41a81c9f84de905a5888cc85cd4c7fd3f21

README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- image-to-text
+task_ids:
+- image-captioning
+paperswithcode_id: sbu-captions-dataset
+pretty_name: SBU Captioned Photo Dataset
+---
+
+# Dataset Card for SBU Captioned Photo Dataset
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [SBU Captioned Photo Dataset homepage](http://www.cs.virginia.edu/~vicente/sbucaptions/)
+- **Repository:**
+- **Paper:** [Im2Text: Describing Images Using 1 Million Captioned Photographs](https://papers.nips.cc/paper/2011/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html)
+- **Leaderboard:**
+- **Point of Contact:** [Vicente Ordóñez Román](mailto:[email protected])
+
+### Dataset Summary
+
+SBU Captioned Photo Dataset is a collection of associated captions and images from Flickr.
+
+### Dataset Preprocessing
+
+This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
+
+```python
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+import io
+import urllib
+
+import PIL
+
+from datasets import load_dataset
+from datasets.utils.file_utils import get_datasets_user_agent
+
+
+def fetch_single_image(image_url, timeout=None, retries=0):
+    for _ in range(retries + 1):
+        try:
+            request = urllib.request.Request(
+                image_url,
+                data=None,
+                headers={"user-agent": get_datasets_user_agent()},
+            )
+            with urllib.request.urlopen(request, timeout=timeout) as req:
+                image = PIL.Image.open(io.BytesIO(req.read()))
+            break
+        except Exception:
+            image = None
+    return image
+
+
+def fetch_images(batch, num_threads, timeout=None, retries=0):
+    fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
+    with ThreadPoolExecutor(max_workers=num_threads) as executor:
+        batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
+    return batch
+
+
+num_threads = 20
+dset = load_dataset("sbu_captions")
+dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
+```
+
+### Supported Tasks and Leaderboards
+
+- `image-to-text`: This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image.
+
+### Languages
+
+All captions are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id:
+
+```
+{
+  'img_url': 'http://static.flickr.com/2723/4385058960_b0f291553e.jpg', 
+  'user_id': '47889917@N08', 
+  'caption': 'A wooden chair in the living room'
+}
+```
+
+### Data Fields
+
+- `image_url`: Static URL for downloading the image associated with the post.
+- `caption`: Textual description of the image.
+- `user_id`: Author of caption.
+
+### Data Splits
+
+All the data is contained in training split. The training set has 1M instances.
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the paper:
+> One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually
+relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.
+
+### Source Data
+
+The source images come from Flickr.
+
+#### Initial Data Collection and Normalization
+
+One key contribution of our paper is a novel web-scale database of photographs with associated
+descriptive text. To enable effective captioning of novel images, this database must be good in two
+ways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The
+captions associated with the data base photographs must be visually relevant so that transferring
+captions between pictures is useful. To achieve the first requirement we query Flickr using a huge
+number of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very
+large, but noisy initial set of photographs with associated text.
+
+#### Who are the source language producers?
+
+The Flickr users.
+
+### Annotations
+
+#### Annotation process
+
+Text descriptions associated with the images are inherited as annotations/captions.
+
+#### Who are the annotators?
+
+The Flickr users.
+
+### Personal and Sensitive Information
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+### Discussion of Biases
+
+### Other Known Limitations
+
+## Additional Information
+
+### Dataset Curators
+
+Vicente Ordonez, Girish Kulkarni and Tamara L. Berg.
+
+### Licensing Information
+
+Not specified.
+
+### Citation Information
+
+```bibtex
+@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}
+}
+```
+
+### Contributions
+
+Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "The SBU Captioned Photo Dataset is a collection of over 1 million images with associated text descriptions extracted from Flicker.\n", "citation": "@inproceedings{NIPS2011_5dd9db5e,\n author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger},\n pages = {},\n publisher = {Curran Associates, Inc.},\n title = {Im2Text: Describing Images Using 1 Million Captioned Photographs},\n url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf},\n volume = {24},\n year = {2011}\n}\n", "homepage": "http://www.cs.virginia.edu/~vicente/sbucaptions", "license": "unknown", "features": {"image_url": {"dtype": "string", "id": null, "_type": "Value"}, "user_id": {"dtype": "string", "id": null, "_type": "Value"}, "caption": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sbu_captioned_photo_dataset", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 143795586, "num_examples": 1000000, "dataset_name": "sbu_captioned_photo_dataset"}}, "download_checksums": {"http://www.cs.virginia.edu/~vicente/sbucaptions/sbu-captions-all.tar.gz": {"num_bytes": 49787719, "checksum": "3d145fb58fea5bf5680e71c82e93d336c1a06d726dbea7f7702d49f5bf2ff532"}}, "download_size": 49787719, "post_processing_size": null, "dataset_size": 143795586, "size_in_bytes": 193583305}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:00d14d8199ccb3ce7cb398c516ccf900acaec507fab3cb3bfab8997011e90520
+size 880

sbu_captions.py

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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 = "http://www.cs.virginia.edu/~vicente/sbucaptions"
+
+_URL = "http://www.cs.virginia.edu/~vicente/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}