Duplicate from google/docci

Co-authored-by: Yasumasa Onoe <yasumasaonoe@users.noreply.huggingface.co>

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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
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+docci_descriptions.jsonlines filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- expert-generated
+- crowdsourced
+language:
+- en
+language_creators:
+- other
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: DOCCI
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags: []
+task_categories:
+- text-to-image
+- image-to-text
+task_ids:
+- image-captioning
+---
+
+# Dataset Card for DOCCI
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks)
+  - [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:** https://google.github.io/docci
+- **Paper:** [arXiv](https://arxiv.org/pdf/2404.19753)
+- **Data Explorer:** [Check images and descriptions](https://google.github.io/docci/viz.html?c=&p=1)
+- **Point of Contact:** docci-dataset@google.com
+- **Report an Error:** [Google Forms](https://forms.gle/v8sUoXWHvuqrWyfe9)
+
+### Dataset Summary
+
+DOCCI (Descriptions of Connected and Contrasting Images) is a collection of images paired with detailed descriptions. The descriptions explain the key elements of the images, as well as secondary information such as background, lighting, and settings. The images are specifically taken to help assess the precise visual properties of images. DOCCI also includes many related images that vary in having key differences from the others. All descriptions are manually annotated to ensure they adequately distinguish each image from its counterparts.
+
+### Supported Tasks
+
+Text-to-Image and Image-to-Text generation
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1536x2048>,
+    'example_id': 'qual_dev_00000',
+    'description': 'An indoor angled down medium close-up front view of a real sized stuffed dog with white and black colored fur wearing a blue hard hat with a light on it. A couple inches to the right of the dog is a real sized black and white penguin that is also wearing a blue hard hat with a light on it. The dog is sitting, and is facing slightly towards the right while looking to its right with its mouth slightly open, showing its pink tongue. The dog and penguin are placed on a gray and white carpet, and placed against a white drawer that has a large gray cushion on top of it. Behind the gray cushion is a transparent window showing green trees on the outside.'
+}
+```
+
+### Data Fields
+
+Name | Explanation
+--- | ---
+`image`       | PIL.JpegImagePlugin.JpegImageFile               
+`example_id`  | The unique ID of an example follows this format: `<SPLIT_NAME>_<EXAMPLE_NUMBER>`.
+`description` | Text description of the associated image.
+
+### Data Splits
+
+Dataset | Train | Test | Qual Dev | Qual Test
+---| ---: | ---: | ---: | ---: 
+DOCCI     | 9,647 | 5,000 | 100 | 100 
+DOCCI-AAR | 4,932 | 5,000 | --  | --
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+DOCCI is designed as an evaluation dataset for both text-to-image (T2I) and image-to-text (I2T) generation. Please see our paper for more details.
+
+### Source Data
+
+#### Initial Data Collection
+
+All images were taken by one of the authors and their family.
+
+### Annotations
+
+#### Annotation process
+
+All text descriptions were written by human annotators.
+We do not rely on any automated process in our data annotation pipeline.
+Please see Appendix A of [our paper](https://arxiv.org/pdf/2404.19753) for details about image curation.
+
+### Personal and Sensitive Information
+
+We manually reviewed all images for personally identifiable information (PII), removing some images and blurring detected faces, phone numbers, and URLs to protect privacy.
+For text descriptions, we instructed annotators to exclude any PII, such as people's names, phone numbers, and URLs.
+After the annotation phase, we employed automatic tools to scan for PII, ensuring the descriptions remained free of such information.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+### Licensing Information
+
+CC BY 4.0
+
+### Citation Information
+
+```
+@inproceedings{OnoeDocci2024,
+  author        = {Yasumasa Onoe and Sunayana Rane and Zachary Berger and Yonatan Bitton and Jaemin Cho and Roopal Garg and
+    Alexander Ku and Zarana Parekh and Jordi Pont-Tuset and Garrett Tanzer and Su Wang and Jason Baldridge},
+  title         = {{DOCCI: Descriptions of Connected and Contrasting Images}},
+  booktitle     = {ECCV},
+  year          = {2024}
+}
+```

docci.py

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+# coding=utf-8
+# Copyright 2022 the HuggingFace Datasets Authors.
+#
+# 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.
+
+import datasets
+import glob
+import json
+import os
+
+from huggingface_hub import hf_hub_url
+
+
+_DESCRIPTION = """
+DOCCI (Descriptions of Connected and Contrasting Images) is a collection of images paired with detailed descriptions. The descriptions explain the key elements of the images, as well as secondary information such as background, lighting, and settings. The images are specifically taken to help assess the precise visual properties of images. DOCCI also includes many related images that vary in having key differences from the others. All descriptions are manually annotated to ensure they adequately distinguish each image from its counterparts.
+"""
+
+_HOMEPAGE = "https://google.github.io/docci/"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://storage.googleapis.com/docci/data/"
+
+_URLS = {
+    "descriptions": _URL + "docci_descriptions.jsonlines",
+    "images": _URL + "docci_images.tar.gz",
+}
+
+_URL_AAR = {
+    "images": _URL + "docci_images_aar.tar.gz"
+}
+
+_FEATURES_DOCCI = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "example_id": datasets.Value('string'),
+        "description": datasets.Value('string'),
+    }
+)
+
+_FEATURES_DOCCI_AAR = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "example_id": datasets.Value('string'),
+    }
+)
+
+
+class DOCCI(datasets.GeneratorBasedBuilder):
+    """DOCCI"""
+    
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="docci", version=VERSION, description="DOCCI images and descriptions"),
+        datasets.BuilderConfig(name="docci_aar", version=VERSION, description="DOCCI-AAR images"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "docci"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=_FEATURES_DOCCI if self.config.name == 'docci' else _FEATURES_DOCCI_AAR,
+            homepage=_HOMEPAGE,
+            description=_DESCRIPTION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        if self.config.name == 'docci':
+            data = dl_manager.download_and_extract(_URLS)
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'data': data, 'split': 'train'}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'data': data, 'split': 'test'}),
+                datasets.SplitGenerator(name=datasets.Split("qual_dev"), gen_kwargs={'data': data, 'split': 'qual_dev'}),
+                datasets.SplitGenerator(name=datasets.Split("qual_test"), gen_kwargs={'data': data, 'split': 'qual_test'}),
+            ]
+        elif self.config.name == 'docci_aar':
+            data = dl_manager.download_and_extract(_URL_AAR)
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'data': data, 'split': 'train'}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'data': data, 'split': 'test'}),
+            ]
+
+    def _generate_examples(self, data, split):
+        if self.config.name == "docci":
+            return self._generate_examples_docci(data, split)
+        elif self.config.name == "docci_aar":
+            return self._generate_examples_docci_aar(data, split)
+
+    def _generate_examples_docci(self, data, split):
+        with open(data["descriptions"], "r") as f:
+            examples = [json.loads(l.strip()) for l in f]
+
+            for ex in examples:
+                if split == "train":
+                    if not (ex["split"] == "train" and ex['example_id'].startswith("train")):
+                        continue
+                elif split == "test":
+                    if not (ex["split"] == "test" and ex['example_id'].startswith("test")):
+                        continue
+                elif split == "qual_dev":
+                    if not (ex["split"] == "qual_dev" and ex['example_id'].startswith("qual_dev")):
+                        continue
+                elif split == "qual_test":
+                    if not (ex["split"] == "qual_test" and ex['example_id'].startswith("qual_test")):
+                        continue
+
+                image_path = os.path.join(data["images"], "images", ex["image_file"])
+
+                _ex = {
+                    "image": image_path,
+                    "example_id": ex["example_id"],
+                    "split": ex["split"],
+                    "image_file": ex["image_file"],
+                    "description": ex["description"],
+                }
+
+                yield _ex["example_id"], _ex
+
+    def _generate_examples_docci_aar(self, data, split):
+        image_files = glob.glob(os.path.join(data["images"], "images_aar", "*.jpg"))
+        
+        for image_path in image_files:
+
+            example_id = os.path.splitext(os.path.basename(image_path))[0]
+            
+            if split == "train":
+                if not example_id.startswith("aar_train"):
+                    continue
+            elif split == "test":
+                if not example_id.startswith("aar_test"):
+                    continue
+
+            _ex = {
+                "image": image_path,
+                "example_id": example_id,
+                "split": split,
+                "image_file": os.path.basename(image_path),
+            }
+
+            yield _ex["example_id"], _ex