Add Visual Genome (#4161)

Commit from https://github.com/huggingface/datasets/commit/1a1d32df517bbf531da1c43a7f421c0dd2beb5d5

README.md

@@ -0,0 +1,460 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+  region_descriptions:
+  - image-to-text
+  objects:
+  - object-detection
+  question_answers:
+  - visual-question-answering
+task_ids:
+  region_descriptions:
+  - image-captioning
+paperswithcode_id: visual-genome
+pretty_name: VisualGenome
+---
+
+# Dataset Card for Visual Genome
+
+## 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:** https://visualgenome.org/
+- **Repository:** 
+- **Paper:** https://visualgenome.org/static/paper/Visual_Genome.pdf
+- **Leaderboard:**
+- **Point of Contact:** ranjaykrishna [at] gmail [dot] com
+
+### Dataset Summary
+
+Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language.
+
+From the paper:
+> Despite progress in perceptual tasks such as
+image classification, computers still perform poorly on
+cognitive tasks such as image description and question
+answering. Cognition is core to tasks that involve not
+just recognizing, but reasoning about our visual world.
+However, models used to tackle the rich content in images for cognitive tasks are still being trained using the
+same datasets designed for perceptual tasks. To achieve
+success at cognitive tasks, models need to understand
+the interactions and relationships between objects in an
+image. When asked “What vehicle is the person riding?”,
+computers will need to identify the objects in an image
+as well as the relationships riding(man, carriage) and
+pulling(horse, carriage) to answer correctly that “the
+person is riding a horse-drawn carriage.”
+
+Visual Genome has:
+ - 108,077 image
+ - 5.4 Million Region Descriptions
+ - 1.7 Million Visual Question Answers
+ - 3.8 Million Object Instances
+ - 2.8 Million Attributes
+ - 2.3 Million Relationships
+
+From the paper:
+> Our dataset contains over 108K images where each
+image has an average of 35 objects, 26 attributes, and 21
+pairwise relationships between objects. We canonicalize
+the objects, attributes, relationships, and noun phrases
+in region descriptions and questions answer pairs to
+WordNet synsets.
+
+### Dataset Preprocessing
+
+### Supported Tasks and Leaderboards
+
+### Languages
+
+All of annotations use English as primary language.
+
+## Dataset Structure
+
+### Data Instances
+
+When loading a specific configuration, users has to append a version dependent suffix:
+```python
+from datasets import load_dataset
+load_dataset("visual_genome", "region_description_v1.2.0")
+```
+
+#### region_descriptions
+
+An example of looks as follows.
+
+```
+{
+  "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>,
+  "image_id": 1,
+  "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg",
+  "width": 800,
+  "height": 600,
+  "coco_id": null,
+  "flickr_id": null,
+  "regions": [
+    {
+      "region_id": 1382,
+      "image_id": 1,
+      "phrase": "the clock is green in colour",
+      "x": 421,
+      "y": 57,
+      "width": 82,
+      "height": 139
+    },
+    ...
+  ]
+}
+```
+
+#### objects
+
+An example of looks as follows.
+
+```
+{
+  "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>,
+  "image_id": 1,
+  "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg",
+  "width": 800,
+  "height": 600,
+  "coco_id": null,
+  "flickr_id": null,
+  "objects": [
+    {
+      "object_id": 1058498,
+      "x": 421,
+      "y": 91,
+      "w": 79,
+      "h": 339,
+      "names": [
+        "clock"
+      ],
+      "synsets": [
+        "clock.n.01"
+      ]
+    },
+    ...
+  ]
+}
+```
+
+#### attributes
+
+An example of looks as follows.
+
+```
+{
+  "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>,
+  "image_id": 1,
+  "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg",
+  "width": 800,
+  "height": 600,
+  "coco_id": null,
+  "flickr_id": null,
+  "attributes": [
+    {
+      "object_id": 1058498,
+      "x": 421,
+      "y": 91,
+      "w": 79,
+      "h": 339,
+      "names": [
+        "clock"
+      ],
+      "synsets": [
+        "clock.n.01"
+      ],
+      "attributes": [
+        "green",
+        "tall"
+      ]
+    },
+    ...
+  }
+]
+```
+
+#### relationships
+
+An example of looks as follows.
+
+```
+{
+  "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>,
+  "image_id": 1,
+  "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg",
+  "width": 800,
+  "height": 600,
+  "coco_id": null,
+  "flickr_id": null,
+  "relationships": [
+    {
+      "relationship_id": 15927,
+      "predicate": "ON",
+      "synsets": "['along.r.01']",
+      "subject": {
+        "object_id": 5045,
+        "x": 119,
+        "y": 338,
+        "w": 274,
+        "h": 192,
+        "names": [
+          "shade"
+        ],
+        "synsets": [
+          "shade.n.01"
+        ]
+      },
+      "object": {
+        "object_id": 5046,
+        "x": 77,
+        "y": 328,
+        "w": 714,
+        "h": 262,
+        "names": [
+          "street"
+        ],
+        "synsets": [
+          "street.n.01"
+        ]
+      }
+    }
+    ...
+  }
+]
+```
+#### question_answers
+
+An example of looks as follows.
+
+```
+{
+  "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>,
+  "image_id": 1,
+  "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg",
+  "width": 800,
+  "height": 600,
+  "coco_id": null,
+  "flickr_id": null,
+  "qas": [
+    {
+      "qa_id": 986768,
+      "image_id": 1,
+      "question": "What color is the clock?",
+      "answer": "Green.",
+      "a_objects": [],
+      "q_objects": []
+    },
+    ...
+  }
+]
+```
+
+### Data Fields
+
+When loading a specific configuration, users has to append a version dependent suffix:
+```python
+from datasets import load_dataset
+load_dataset("visual_genome", "region_description_v1.2.0")
+```
+
+#### region_descriptions
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `image_id`: Unique numeric ID of the image.
+- `url`:  URL of source image.
+- `width`: Image width.
+- `height`: Image height.
+- `coco_id`: Id mapping to MSCOCO indexing.
+- `flickr_id`: Id mapping to Flicker indexing.
+- `regions`: Holds a list of `Region` dataclasses:
+  - `region_id`: Unique numeric ID of the region.
+  - `image_id`: Unique numeric ID of the image.
+  - `x`: x coordinate of bounding box's top left corner.
+  - `y`: y coordinate of bounding box's top left corner.
+  - `width`: Bounding box width.
+  - `height`: Bounding box height.
+
+#### objects
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `image_id`: Unique numeric ID of the image.
+- `url`:  URL of source image.
+- `width`: Image width.
+- `height`: Image height.
+- `coco_id`: Id mapping to MSCOCO indexing.
+- `flickr_id`: Id mapping to Flicker indexing.
+- `objects`: Holds a list of `Object` dataclasses:
+  - `object_id`: Unique numeric ID of the object.
+  - `x`: x coordinate of bounding box's top left corner.
+  - `y`: y coordinate of bounding box's top left corner.
+  - `w`: Bounding box width.
+  - `h`: Bounding box height.
+  - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg
+  - `synsets`: List of `WordNet synsets`.
+
+#### attributes
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `image_id`: Unique numeric ID of the image.
+- `url`:  URL of source image.
+- `width`: Image width.
+- `height`: Image height.
+- `coco_id`: Id mapping to MSCOCO indexing.
+- `flickr_id`: Id mapping to Flicker indexing.
+- `attributes`: Holds a list of `Object` dataclasses:
+  - `object_id`: Unique numeric ID of the region.
+  - `x`: x coordinate of bounding box's top left corner.
+  - `y`: y coordinate of bounding box's top left corner.
+  - `w`: Bounding box width.
+  - `h`: Bounding box height.
+  - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg
+  - `synsets`: List of `WordNet synsets`.
+  - `attributes`: List of attributes associated with the object.
+
+#### relationships
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `image_id`: Unique numeric ID of the image.
+- `url`:  URL of source image.
+- `width`: Image width.
+- `height`: Image height.
+- `coco_id`: Id mapping to MSCOCO indexing.
+- `flickr_id`: Id mapping to Flicker indexing.
+- `relationships`: Holds a list of `Relationship` dataclasses:
+  - `relationship_id`: Unique numeric ID of the object.
+  - `predicate`: Predicate defining relationship between a subject and an object.
+  - `synsets`: List of `WordNet synsets`.
+  - `subject`: Object dataclass. See subsection on `objects`.
+  - `object`: Object dataclass. See subsection on `objects`.
+
+#### question_answers
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `image_id`: Unique numeric ID of the image.
+- `url`:  URL of source image.
+- `width`: Image width.
+- `height`: Image height.
+- `coco_id`: Id mapping to MSCOCO indexing.
+- `flickr_id`: Id mapping to Flicker indexing.
+- `qas`: Holds a list of `Question-Answering` dataclasses:
+  - `qa_id`: Unique numeric ID of the question-answer pair.
+  - `image_id`: Unique numeric ID of the image.
+  - `question`: Question.
+  - `answer`: Answer.
+  - `q_objects`: List of object dataclass associated with `question` field. See subsection on `objects`.
+  - `a_objects`: List of object dataclass associated with `answer` field. See subsection on `objects`.
+
+### Data Splits
+
+All the data is contained in training set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+#### Who are the source language producers?
+
+### Annotations
+
+#### Annotation process
+
+#### Who are the annotators?
+
+From the paper:
+> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over
+33, 000 unique workers contributed to the dataset. The
+dataset was collected over the course of 6 months after
+15 months of experimentation and iteration on the data
+representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where
+each HIT involved creating descriptions, questions and
+answers, or region graphs. Each HIT was designed such
+that workers manage to earn anywhere between $6-$8
+per hour if they work continuously, in line with ethical
+research standards on Mechanical Turk (Salehi et al.,
+2015). Visual Genome HITs achieved a 94.1% retention
+rate, meaning that 94.1% of workers who completed one
+of our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States.
+The majority of our workers were
+between the ages of 25 and 34 years old. Our youngest
+contributor was 18 years and the oldest was 68 years
+old. We also had a near-balanced split of 54.15% male
+and 45.85% female workers.
+
+### Personal and Sensitive Information
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+### Discussion of Biases
+
+### Other Known Limitations
+
+## Additional Information
+
+### Dataset Curators
+
+### Licensing Information
+
+Visual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License.
+
+### Citation Information
+
+```bibtex
+@inproceedings{krishnavisualgenome,
+  title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
+  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and Bernstein, Michael and Fei-Fei, Li},
+  year = {2016},
+  url = {https://arxiv.org/abs/1602.07332},
+}
+```
+
+### Contributions
+
+Due to limitation of the dummy_data creation, we provide a `fix_generated_dummy_data.py` script that fix the dataset in-place.
+
+Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.

dataset_infos.json

@@ -0,0 +1 @@
+{"region_descriptions_v1.0.0": {"description": "Visual Genome enable to model objects and relationships between objects.\nThey collect dense annotations of objects, attributes, and relationships within each image.\nSpecifically, the dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects.\n", "citation": "@inproceedings{krishnavisualgenome,\n  title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},\n  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and Bernstein, Michael and Fei-Fei, Li},\n  year = {2016},\n  url = {https://arxiv.org/abs/1602.07332},\n}\n", "homepage": "https://visualgenome.org/", "license": "Creative Commons Attribution 4.0 International License", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "image_id": {"dtype": "int32", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "width": {"dtype": "int32", "id": null, "_type": "Value"}, "height": {"dtype": "int32", "id": null, "_type": "Value"}, "coco_id": {"dtype": "int64", "id": null, "_type": "Value"}, "flickr_id": {"dtype": "int64", "id": null, "_type": "Value"}, "regions": [{"region_id": {"dtype": "int32", "id": null, "_type": "Value"}, "image_id": {"dtype": "int32", "id": null, "_type": "Value"}, "phrase": {"dtype": "string", "id": null, "_type": "Value"}, "x": {"dtype": "int32", "id": null, "_type": "Value"}, "y": {"dtype": "int32", "id": null, "_type": "Value"}, "width": {"dtype": "int32", "id": null, "_type": "Value"}, "height": {"dtype": "int32", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "visual_genome", "config_name": "region_descriptions_v1.0.0", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 260873884, "num_examples": 108077, "dataset_name": "visual_genome"}}, "download_checksums": {"https://visualgenome.org/static/data/dataset/image_data.json.zip": {"num_bytes": 1780854, "checksum": "b87a94918cb2ff4d952cf1dfeca0b9cf6cd6fd204c2f8704645653be1163681a"}, "https://visualgenome.org/static/data/dataset/region_descriptions_v1.json.zip": {"num_bytes": 99460401, "checksum": "9e54cd76082f7ce5168a1779fc8c3d0629720492ec0fa12f2d8339c3e0dc9734"}, "https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip": {"num_bytes": 9731705982, "checksum": "51c682d2721f880150720bb416e0346a4c787e4c55d7f80dfd1bd3f73ba81646"}, "https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip": {"num_bytes": 5471658058, "checksum": "99da1a0ddf87011319ff3b05cf9176ffee2731cc3c52951162d9ef0d68e3cfb5"}}, "download_size": 15304605295, "post_processing_size": null, "dataset_size": 260873884, "size_in_bytes": 15565479179}}

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fix_generated_dummy_data.py

@@ -0,0 +1,48 @@
+import json
+import re
+from pathlib import Path
+from zipfile import ZipFile
+
+
+def main():
+    dummy_dir = Path(__file__).parent / "dummy"
+    config_paths = list(dummy_dir.iterdir())
+
+    for config in config_paths:
+        versions = list(config.iterdir())
+        assert len(versions) == 1, versions
+        version = versions[0]
+        zip_filepath = version / "dummy_data.zip"
+
+        # We need to open the zip file
+        with ZipFile(zip_filepath, "r") as zip_dir:
+            with zip_dir.open("dummy_data/image_data.json.zip/image_data.json", "r") as fi:
+                image_metadatas = json.load(fi)
+
+        default_jpg_path = Path(__file__).parent / "huggingface.jpg"
+        with ZipFile(zip_filepath, "a") as zip_dir:
+            for image_metadata in image_metadatas:
+                url = image_metadata["url"]
+
+                matches = re.match(r"https://cs.stanford.edu/people/rak248/VG_100K(?:_(2))?/[0-9]+.jpg", url)
+                assert matches is not None
+
+                # Find where locally the images should be
+                vg_version = matches.group(1)
+                if vg_version is None:
+                    local_path = re.sub(
+                        "https://cs.stanford.edu/people/rak248/VG_100K", "dummy_data/images.zip/VG_100K", url
+                    )
+                else:
+                    local_path = re.sub(
+                        f"https://cs.stanford.edu/people/rak248/VG_100K_{vg_version}",
+                        f"dummy_data/images{vg_version}.zip/VG_100K_{vg_version}",
+                        url,
+                    )
+
+                # Write those images.
+                zip_dir.write(filename=default_jpg_path, arcname=local_path)
+
+
+if __name__ == "__main__":
+    main()

visual_genome.py

@@ -0,0 +1,465 @@
+# 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.
+"""Visual Genome dataset."""
+
+import json
+import os
+import re
+from collections import defaultdict
+from typing import Any, Callable, Dict, Optional
+from urllib.parse import urlparse
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@inproceedings{krishnavisualgenome,
+  title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
+  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and Bernstein, Michael and Fei-Fei, Li},
+  year = {2016},
+  url = {https://arxiv.org/abs/1602.07332},
+}
+"""
+
+_DESCRIPTION = """\
+Visual Genome enable to model objects and relationships between objects.
+They collect dense annotations of objects, attributes, and relationships within each image.
+Specifically, the dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects.
+"""
+
+_HOMEPAGE = "https://visualgenome.org/"
+
+_LICENSE = "Creative Commons Attribution 4.0 International License"
+
+_BASE_IMAGE_URLS = {
+    "https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip": "VG_100K",
+    "https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip": "VG_100K_2",
+}
+
+_LATEST_VERSIONS = {
+    "region_descriptions": "1.2.0",
+    "objects": "1.4.0",
+    "attributes": "1.2.0",
+    "relationships": "1.4.0",
+    "question_answers": "1.2.0",
+    "image_metadata": "1.2.0",
+}
+
+# ---- Features ----
+
+_BASE_IMAGE_METADATA_FEATURES = {
+    "image_id": datasets.Value("int32"),
+    "url": datasets.Value("string"),
+    "width": datasets.Value("int32"),
+    "height": datasets.Value("int32"),
+    "coco_id": datasets.Value("int64"),
+    "flickr_id": datasets.Value("int64"),
+}
+
+_BASE_SYNTET_FEATURES = {
+    "synset_name": datasets.Value("string"),
+    "entity_name": datasets.Value("string"),
+    "entity_idx_start": datasets.Value("int32"),
+    "entity_idx_end": datasets.Value("int32"),
+}
+
+_BASE_OBJECT_FEATURES = {
+    "object_id": datasets.Value("int32"),
+    "x": datasets.Value("int32"),
+    "y": datasets.Value("int32"),
+    "w": datasets.Value("int32"),
+    "h": datasets.Value("int32"),
+    "names": [datasets.Value("string")],
+    "synsets": [datasets.Value("string")],
+}
+
+_BASE_QA_OBJECT_FEATURES = {
+    "object_id": datasets.Value("int32"),
+    "x": datasets.Value("int32"),
+    "y": datasets.Value("int32"),
+    "w": datasets.Value("int32"),
+    "h": datasets.Value("int32"),
+    "names": [datasets.Value("string")],
+    "synsets": [datasets.Value("string")],
+}
+
+_BASE_QA_OBJECT = {
+    "qa_id": datasets.Value("int32"),
+    "image_id": datasets.Value("int32"),
+    "question": datasets.Value("string"),
+    "answer": datasets.Value("string"),
+    "a_objects": [_BASE_QA_OBJECT_FEATURES],
+    "q_objects": [_BASE_QA_OBJECT_FEATURES],
+}
+
+_BASE_REGION_FEATURES = {
+    "region_id": datasets.Value("int32"),
+    "image_id": datasets.Value("int32"),
+    "phrase": datasets.Value("string"),
+    "x": datasets.Value("int32"),
+    "y": datasets.Value("int32"),
+    "width": datasets.Value("int32"),
+    "height": datasets.Value("int32"),
+}
+
+_BASE_RELATIONSHIP_FEATURES = {
+    "relationship_id": datasets.Value("int32"),
+    "predicate": datasets.Value("string"),
+    "synsets": datasets.Value("string"),
+    "subject": _BASE_OBJECT_FEATURES,
+    "object": _BASE_OBJECT_FEATURES,
+}
+
+_NAME_VERSION_TO_ANNOTATION_FEATURES = {
+    "region_descriptions": {
+        "1.2.0": {"regions": [_BASE_REGION_FEATURES]},
+        "1.0.0": {"regions": [_BASE_REGION_FEATURES]},
+    },
+    "objects": {
+        "1.4.0": {"objects": [{**_BASE_OBJECT_FEATURES, "merged_object_ids": [datasets.Value("int32")]}]},
+        "1.2.0": {"objects": [_BASE_OBJECT_FEATURES]},
+        "1.0.0": {"objects": [_BASE_OBJECT_FEATURES]},
+    },
+    "attributes": {
+        "1.2.0": {"attributes": [{**_BASE_OBJECT_FEATURES, "attributes": [datasets.Value("string")]}]},
+        "1.0.0": {"attributes": [{**_BASE_OBJECT_FEATURES, "attributes": [datasets.Value("string")]}]},
+    },
+    "relationships": {
+        "1.4.0": {
+            "relationships": [
+                {
+                    **_BASE_RELATIONSHIP_FEATURES,
+                    "subject": {**_BASE_OBJECT_FEATURES, "merged_object_ids": [datasets.Value("int32")]},
+                    "object": {**_BASE_OBJECT_FEATURES, "merged_object_ids": [datasets.Value("int32")]},
+                }
+            ]
+        },
+        "1.2.0": {"relationships": [_BASE_RELATIONSHIP_FEATURES]},
+        "1.0.0": {"relationships": [_BASE_RELATIONSHIP_FEATURES]},
+    },
+    "question_answers": {"1.2.0": {"qas": [_BASE_QA_OBJECT]}, "1.0.0": {"qas": [_BASE_QA_OBJECT]}},
+}
+
+# ----- Helpers -----
+
+
+def _get_decompressed_filename_from_url(url: str) -> str:
+    parsed_url = urlparse(url)
+    compressed_filename = os.path.basename(parsed_url.path)
+
+    # Remove `.zip` suffix
+    assert compressed_filename.endswith(".zip")
+    uncompressed_filename = compressed_filename[:-4]
+
+    # Remove version.
+    unversioned_uncompressed_filename = re.sub(r"_v[0-9]+(?:_[0-9]+)?\.json$", ".json", uncompressed_filename)
+
+    return unversioned_uncompressed_filename
+
+
+def _get_local_image_path(img_url: str, folder_local_paths: Dict[str, str]) -> str:
+    """
+    Obtain image folder given an image url.
+
+    For example:
+      Given `https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg` as an image url, this method returns the local path for that image.
+    """
+    matches = re.fullmatch(r"^https://cs.stanford.edu/people/rak248/(VG_100K(?:_2)?)/([0-9]+\.jpg)$", img_url)
+    assert matches is not None, f"Got img_url: {img_url}, matched: {matches}"
+    folder, filename = matches.group(1), matches.group(2)
+    return os.path.join(folder_local_paths[folder], filename)
+
+
+# ----- Annotation normalizers ----
+
+_BASE_ANNOTATION_URL = "https://visualgenome.org/static/data/dataset"
+
+
+def _normalize_region_description_annotation_(annotation: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalizes region descriptions annotation in-place"""
+    # Some attributes annotations don't have an attribute field
+    for region in annotation["regions"]:
+        # `id` should be converted to `region_id`:
+        if "id" in region:
+            region["region_id"] = region["id"]
+            del region["id"]
+
+        # `image` should be converted to `image_id`
+        if "image" in region:
+            region["image_id"] = region["image"]
+            del region["image"]
+
+    return annotation
+
+
+def _normalize_object_annotation_(annotation: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalizes object annotation in-place"""
+    # Some attributes annotations don't have an attribute field
+    for object_ in annotation["objects"]:
+        # `id` should be converted to `object_id`:
+        if "id" in object_:
+            object_["object_id"] = object_["id"]
+            del object_["id"]
+
+        # Some versions of `object` annotations don't have `synsets` field.
+        if "synsets" not in object_:
+            object_["synsets"] = None
+
+    return annotation
+
+
+def _normalize_attribute_annotation_(annotation: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalizes attributes annotation in-place"""
+    # Some attributes annotations don't have an attribute field
+    for attribute in annotation["attributes"]:
+        # `id` should be converted to `object_id`:
+        if "id" in attribute:
+            attribute["object_id"] = attribute["id"]
+            del attribute["id"]
+
+        # `objects_names` should be convered to `names:
+        if "object_names" in attribute:
+            attribute["names"] = attribute["object_names"]
+            del attribute["object_names"]
+
+        # Some versions of `attribute` annotations don't have `synsets` field.
+        if "synsets" not in attribute:
+            attribute["synsets"] = None
+
+        # Some versions of `attribute` annotations don't have `attributes` field.
+        if "attributes" not in attribute:
+            attribute["attributes"] = None
+
+    return annotation
+
+
+def _normalize_relationship_annotation_(annotation: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalizes relationship annotation in-place"""
+    # For some reason relationships objects have a single name instead of a list of names.
+    for relationship in annotation["relationships"]:
+        # `id` should be converted to `object_id`:
+        if "id" in relationship:
+            relationship["relationship_id"] = relationship["id"]
+            del relationship["id"]
+
+        if "synsets" not in relationship:
+            relationship["synsets"] = None
+
+        subject = relationship["subject"]
+        object_ = relationship["object"]
+
+        for obj in [subject, object_]:
+            # `id` should be converted to `object_id`:
+            if "id" in obj:
+                obj["object_id"] = obj["id"]
+                del obj["id"]
+
+            if "name" in obj:
+                obj["names"] = [obj["name"]]
+                del obj["name"]
+
+            if "synsets" not in obj:
+                obj["synsets"] = None
+
+    return annotation
+
+
+def _normalize_image_metadata_(image_metadata: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalizes image metadata in-place"""
+    if "id" in image_metadata:
+        image_metadata["image_id"] = image_metadata["id"]
+        del image_metadata["id"]
+    return image_metadata
+
+
+_ANNOTATION_NORMALIZER = defaultdict(lambda: lambda x: x)
+_ANNOTATION_NORMALIZER.update(
+    {
+        "region_descriptions": _normalize_region_description_annotation_,
+        "objects": _normalize_object_annotation_,
+        "attributes": _normalize_attribute_annotation_,
+        "relationships": _normalize_relationship_annotation_,
+    }
+)
+
+# ---- Visual Genome loading script ----
+
+
+class VisualGenomeConfig(datasets.BuilderConfig):
+    """BuilderConfig for Visual Genome."""
+
+    def __init__(self, name: str, version: Optional[str] = None, with_image: bool = True, **kwargs):
+        _version = _LATEST_VERSIONS[name] if version is None else version
+        _name = f"{name}_v{_version}"
+        super(VisualGenomeConfig, self).__init__(version=datasets.Version(_version), name=_name, **kwargs)
+        self._name_without_version = name
+        self.annotations_features = _NAME_VERSION_TO_ANNOTATION_FEATURES[self._name_without_version][
+            self.version.version_str
+        ]
+        self.with_image = with_image
+
+    @property
+    def annotations_url(self):
+        if self.version.match(_LATEST_VERSIONS[self._name_without_version]):
+            return f"{_BASE_ANNOTATION_URL}/{self._name_without_version}.json.zip"
+
+        major, minor = self.version.major, self.version.minor
+        if minor == 0:
+            return f"{_BASE_ANNOTATION_URL}/{self._name_without_version}_v{major}.json.zip"
+        else:
+            return f"{_BASE_ANNOTATION_URL}/{self._name_without_version}_v{major}_{minor}.json.zip"
+
+    @property
+    def image_metadata_url(self):
+        if not self.version.match(_LATEST_VERSIONS["image_metadata"]):
+            logger.warning(
+                f"Latest image metadata version is {_LATEST_VERSIONS['image_metadata']}. Trying to generate a dataset of version: {self.version}. Please double check that image data are unchanged between the two versions."
+            )
+        return f"{_BASE_ANNOTATION_URL}/image_data.json.zip"
+
+    @property
+    def features(self):
+        return datasets.Features(
+            {
+                **({"image": datasets.Image()} if self.with_image else {}),
+                **_BASE_IMAGE_METADATA_FEATURES,
+                **self.annotations_features,
+            }
+        )
+
+
+class VisualGenome(datasets.GeneratorBasedBuilder):
+    """Visual Genome dataset."""
+
+    BUILDER_CONFIG_CLASS = VisualGenomeConfig
+    BUILDER_CONFIGS = [
+        *[VisualGenomeConfig(name="region_descriptions", version=version) for version in ["1.0.0", "1.2.0"]],
+        *[VisualGenomeConfig(name="question_answers", version=version) for version in ["1.0.0", "1.2.0"]],
+        *[
+            VisualGenomeConfig(name="objects", version=version)
+            # TODO: add support for 1.4.0
+            for version in ["1.0.0", "1.2.0"]
+        ],
+        *[VisualGenomeConfig(name="attributes", version=version) for version in ["1.0.0", "1.2.0"]],
+        *[
+            VisualGenomeConfig(name="relationships", version=version)
+            # TODO: add support for 1.4.0
+            for version in ["1.0.0", "1.2.0"]
+        ],
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=self.config.features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Download image meta datas.
+        image_metadatas_dir = dl_manager.download_and_extract(self.config.image_metadata_url)
+        image_metadatas_file = os.path.join(
+            image_metadatas_dir, _get_decompressed_filename_from_url(self.config.image_metadata_url)
+        )
+
+        # Download annotations
+        annotations_dir = dl_manager.download_and_extract(self.config.annotations_url)
+        annotations_file = os.path.join(
+            annotations_dir, _get_decompressed_filename_from_url(self.config.annotations_url)
+        )
+
+        # Optionally download images
+        if self.config.with_image:
+            image_folder_keys = list(_BASE_IMAGE_URLS.keys())
+            image_dirs = dl_manager.download_and_extract(image_folder_keys)
+            image_folder_local_paths = {
+                _BASE_IMAGE_URLS[key]: os.path.join(dir_, _BASE_IMAGE_URLS[key])
+                for key, dir_ in zip(image_folder_keys, image_dirs)
+            }
+        else:
+            image_folder_local_paths = None
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_folder_local_paths": image_folder_local_paths,
+                    "image_metadatas_file": image_metadatas_file,
+                    "annotations_file": annotations_file,
+                    "annotation_normalizer_": _ANNOTATION_NORMALIZER[self.config._name_without_version],
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        image_folder_local_paths: Optional[Dict[str, str]],
+        image_metadatas_file: str,
+        annotations_file: str,
+        annotation_normalizer_: Callable[[Dict[str, Any]], Dict[str, Any]],
+    ):
+        with open(annotations_file, "r", encoding="utf-8") as fi:
+            annotations = json.load(fi)
+
+        with open(image_metadatas_file, "r", encoding="utf-8") as fi:
+            image_metadatas = json.load(fi)
+
+        assert len(image_metadatas) == len(annotations)
+        for idx, (image_metadata, annotation) in enumerate(zip(image_metadatas, annotations)):
+            # in-place operation to normalize image_metadata
+            _normalize_image_metadata_(image_metadata)
+
+            # Normalize image_id across all annotations
+            if "id" in annotation:
+                # annotation["id"] corresponds to image_metadata["image_id"]
+                assert (
+                    image_metadata["image_id"] == annotation["id"]
+                ), f"Annotations doesn't match with image metadataset. Got image_metadata['image_id']: {image_metadata['image_id']} and annotations['id']: {annotation['id']}"
+                del annotation["id"]
+            else:
+                assert "image_id" in annotation
+                assert (
+                    image_metadata["image_id"] == annotation["image_id"]
+                ), f"Annotations doesn't match with image metadataset. Got image_metadata['image_id']: {image_metadata['image_id']} and annotations['image_id']: {annotation['image_id']}"
+
+            # Normalize image_id across all annotations
+            if "image_url" in annotation:
+                # annotation["image_url"] corresponds to image_metadata["url"]
+                assert (
+                    image_metadata["url"] == annotation["image_url"]
+                ), f"Annotations doesn't match with image metadataset. Got image_metadata['url']: {image_metadata['url']} and annotations['image_url']: {annotation['image_url']}"
+                del annotation["image_url"]
+            elif "url" in annotation:
+                # annotation["url"] corresponds to image_metadata["url"]
+                assert (
+                    image_metadata["url"] == annotation["url"]
+                ), f"Annotations doesn't match with image metadataset. Got image_metadata['url']: {image_metadata['url']} and annotations['url']: {annotation['url']}"
+
+            # in-place operation to normalize annotations
+            annotation_normalizer_(annotation)
+
+            # optionally add image to the annotation
+            if image_folder_local_paths is not None:
+                filepath = _get_local_image_path(image_metadata["url"], image_folder_local_paths)
+                image_dict = {"image": filepath}
+            else:
+                image_dict = {}
+
+            yield idx, {**image_dict, **image_metadata, **annotation}