Datasets:

ranjaykrishna
/

visual_genome

	---
	annotations_creators:
	- found
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- image-to-text
	- object-detection
	- visual-question-answering
	task_ids:
	- image-captioning
	paperswithcode_id: visual-genome
	pretty_name: VisualGenome
	dataset_info:
	features:
	- name: image
	dtype: image
	- name: image_id
	dtype: int32
	- name: url
	dtype: string
	- name: width
	dtype: int32
	- name: height
	dtype: int32
	- name: coco_id
	dtype: int64
	- name: flickr_id
	dtype: int64
	- name: regions
	list:
	- name: region_id
	dtype: int32
	- name: image_id
	dtype: int32
	- name: phrase
	dtype: string
	- name: x
	dtype: int32
	- name: y
	dtype: int32
	- name: width
	dtype: int32
	- name: height
	dtype: int32
	config_name: region_descriptions_v1.0.0
	splits:
	- name: train
	num_bytes: 260873884
	num_examples: 108077
	download_size: 15304605295
	dataset_size: 260873884
	config_names:
	- objects
	- question_answers
	- region_descriptions
	---

	# 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://homes.cs.washington.edu/~ranjay/visualgenome/
	- Repository:
	- Paper: https://doi.org/10.1007/s11263-016-0981-7
	- 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
	@article{Krishna2016VisualGC,
	title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
	author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Michael S. Bernstein and Li Fei-Fei},
	journal={International Journal of Computer Vision},
	year={2017},
	volume={123},
	pages={32-73},
	url={https://doi.org/10.1007/s11263-016-0981-7},
	doi={10.1007/s11263-016-0981-7}
	}
	```

	### 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.