Add Conceptual 12M (#4162)

* Add Conceptual 12M

Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

Commit from https://github.com/huggingface/datasets/commit/9c8c8d6cb41d57a79113d7d1f252e0d6160c9edc

README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- image-to-text
+task_ids:
+- image-captioning
+paperswithcode_id: cc12m
+pretty_name: Conceptual 12M
+---
+
+# Dataset Card for Conceptual 12M
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
+- **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981)
+- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
+
+### Dataset Summary
+
+Conceptual 12M (CC12M) is a dataset with 12 million  image-text pairs specifically meant to be used for visionand-language pre-training.
+Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).
+
+### 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.Image
+
+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("conceptual_12m")
+dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
+```
+
+### Supported Tasks and Leaderboards
+
+- `image-captioning`: This dataset can be used to train model for the Image Captioning task.
+
+### Languages
+
+All captions are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance represents a single image with a caption:
+
+```
+{
+  'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
+  'caption': 'a very typical bus station'
+}
+```
+
+### Data Fields
+
+- `image_url`: Static URL for downloading the image associated with the post.
+- `caption`: Textual description of the image.
+
+### Data Splits
+
+There is only training data, with a total of 12423374 rows
+
+## Dataset Creation
+
+### Curation Rationale
+
+Conceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+From the paper:
+> To arrive at CC12M, we keep
+the image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2. 
+We still keep only JPEG images with size greater than
+400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text
+between 3 and 256 words in the alt-text. We still discard
+candidates with no noun or no determiner, but permit ones
+without prepositions. We discard the heuristics regarding
+high unique-word ratio covering various POS tags and word
+capitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the
+above relaxations, the threshold for counting a word type as
+rare is increased from 5 to 20
+
+> The main motivation for CC3M to
+perform text transformation is that a majority of candidate
+captions contain ultrafine-grained entities such as proper
+names (people, venues, locations, etc.), making it extremely
+difficult to learn as part of the image captioning task. In
+contrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.
+We thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as
+raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
+of the individuals in these images. For this step, we use the
+Google Cloud Natural Language APIs to detect all named
+entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
+are transformed in this fashion.
+
+#### Who are the source language producers?
+
+Not specified.
+
+### Annotations
+
+#### Annotation process
+
+Annotations are extracted jointly with the images using the automatic pipeline.
+
+#### Who are the annotators?
+
+Not specified.
+
+### Personal and Sensitive Information
+
+From the paper:
+
+> The only exception to the “keep alt-texts as
+raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
+of the individuals in these images. For this step, we use the
+Google Cloud Natural Language APIs to detect all named
+entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
+are transformed in this fashion.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.
+
+### Licensing Information
+
+The dataset may be freely used for any purpose, although acknowledgement of
+Google LLC ("Google") as the data source would be appreciated. The dataset is
+provided "AS IS" without any warranty, express or implied. Google disclaims all
+liability for any damages, direct or indirect, resulting from the use of the
+dataset.
+
+### Citation Information
+
+```bibtex
+@inproceedings{changpinyo2021cc12m,
+  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
+  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
+  booktitle = {CVPR},
+  year = {2021},
+}
+```
+
+### Contributions
+
+Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.

conceptual_12m.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.
+"""Conceptual 12M dataset."""
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{changpinyo2021cc12m,
+  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
+  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
+  booktitle = {CVPR},
+  year = {2021},
+}
+"""
+
+_DESCRIPTION = """\
+Conceptual 12M is a large-scale dataset of 12 million
+image-text pairs specifically meant to be used for visionand-language pre-training.
+Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/conceptual-12m"
+
+_LICENSE = """\
+The dataset may be freely used for any purpose, although acknowledgement of
+Google LLC ("Google") as the data source would be appreciated. The dataset is
+provided "AS IS" without any warranty, express or implied. Google disclaims all
+liability for any damages, direct or indirect, resulting from the use of the
+dataset.
+"""
+
+_URL = "https://storage.googleapis.com/conceptual_12m/cc12m.tsv"
+
+
+class Conceptual12M(datasets.GeneratorBasedBuilder):
+    """Conceptual 12M dataset."""
+
+    def _info(self):
+        features = datasets.Features({"image_url": datasets.Value("string"), "caption": datasets.Value("string")})
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        file = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "file": file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, file):
+        with open(file, "r", encoding="utf-8") as fi:
+            for idx, line in enumerate(fi):
+                image_url, caption = line.split("\t", maxsplit=1)
+                yield idx, {"image_url": image_url, "caption": caption}

dataset_infos.json

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+{"default": {"description": "Conceptual 12M is a large-scale dataset of 12 million\nimage-text pairs specifically meant to be used for visionand-language pre-training.\nIts data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M.\n", "citation": "@inproceedings{changpinyo2021cc12m,\n  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},\n  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},\n  booktitle = {CVPR},\n  year = {2021},\n}\n", "homepage": "https://github.com/google-research-datasets/conceptual-12m", "license": "The dataset may be freely used for any purpose, although acknowledgement of\nGoogle LLC (\"Google\") as the data source would be appreciated. The dataset is\nprovided \"AS IS\" without any warranty, express or implied. Google disclaims all\nliability for any damages, direct or indirect, resulting from the use of the\ndataset.\n", "features": {"image_url": {"dtype": "string", "id": null, "_type": "Value"}, "caption": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "conceptual12_m", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2794168030, "num_examples": 12423374, "dataset_name": "conceptual12_m"}}, "download_checksums": {"https://storage.googleapis.com/conceptual_12m/cc12m.tsv": {"num_bytes": 2707204412, "checksum": "892b549d493c7e75ade10d46c88c9ddabb097790d912b74cfc0ea4ff035ec2c3"}}, "download_size": 2707204412, "post_processing_size": null, "dataset_size": 2794168030, "size_in_bytes": 5501372442}}

dummy/0.0.0/dummy_data.zip

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