| --- |
| 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. |
