Datasets:
Tasks:
Visual Question Answering
Formats:
csv
Sub-tasks:
visual-question-answering
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
toloka
License:
annotations_creators: | |
- crowdsourced | |
language: | |
- en | |
language_creators: | |
- crowdsourced | |
license: | |
- cc-by-4.0 | |
multilinguality: | |
- monolingual | |
pretty_name: WSDMCup2023 | |
size_categories: | |
- 10K<n<100K | |
source_datasets: [] | |
tags: | |
- toloka | |
task_categories: | |
- visual-question-answering | |
task_ids: | |
- visual-question-answering | |
dataset_info: | |
features: | |
- name: image | |
dtype: string | |
- name: width | |
dtype: int64 | |
- name: height | |
dtype: int64 | |
- name: left | |
dtype: int64 | |
- name: top | |
dtype: int64 | |
- name: right | |
dtype: int64 | |
- name: bottom | |
dtype: int64 | |
- name: question | |
dtype: string | |
splits: | |
- name: train | |
num_examples: 38990 | |
- name: train_sample | |
num_examples: 1000 | |
- name: test_public | |
num_examples: 1705 | |
- name: test_private | |
num_examples: 4504 | |
config_name: wsdmcup2023 | |
# Dataset Card for WSDMCup2023 | |
## Dataset Description | |
- **Homepage:** [Toloka Visual Question Answering Challenge](https://toloka.ai/challenges/wsdm2023) | |
- **Repository:** [WSDM Cup 2023 Starter Pack](https://github.com/Toloka/WSDMCup2023) | |
- **Paper:** <https://arxiv.org/abs/2309.16511> | |
- **Leaderboard:** [CodaLab Competition Leaderboard](https://codalab.lisn.upsaclay.fr/competitions/7434#results) | |
- **Point of Contact:** [email protected] | |
| Question | Image and Answer | | |
| --- | --- | | |
| What do you use to hit the ball? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/KUsGAc_eqdMcNxkBXzzl/KUsGAc_eqdMcNxkBXzzl_webp_1280_x2.webp" width="228" alt="What do you use to hit the ball?"> | | |
| What do people use for cutting? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/brXEVYckNLfQKcfNu4DF/brXEVYckNLfQKcfNu4DF_webp_1280_x2.webp" width="228" alt="What do people use for cutting?"> | | |
| What do we use to support the immune system and get vitamin C? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/HQ0A-ZvZCGCmYfTs83K7/HQ0A-ZvZCGCmYfTs83K7_webp_1280_x2.webp" width="228" alt="What do we use to support the immune system and get vitamin C?"> | | |
### Dataset Summary | |
The WSDMCup2023 Dataset consists of images associated with textual questions. | |
One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing | |
the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in | |
Context dataset, [MS COCO](https://cocodataset.org/). All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/). | |
Our dataset has 45,199 instances split among three subsets: train (38,990 instances), public test (1,705 instances), | |
and private test (4,504 instances). The entire train dataset was available for everyone since the start of the challenge. | |
The public test dataset was available since the evaluation phase of the competition but without any ground truth labels. | |
After the end of the competition, public and private sets were released. | |
## Dataset Citation | |
Please cite the challenge results or dataset description as follows. | |
- Ustalov D., Pavlichenko N., Koshelev S., Likhobaba D., and Smirnova A. [Toloka Visual Question Answering Benchmark](https://arxiv.org/abs/2309.16511). 2023. arXiv: [2309.16511 [cs.CV]](https://arxiv.org/abs/2309.16511). | |
```bibtex | |
@inproceedings{TolokaWSDMCup2023, | |
author = {Ustalov, Dmitry and Pavlichenko, Nikita and Koshelev, Sergey and Likhobaba, Daniil and Smirnova, Alisa}, | |
title = {{Toloka Visual Question Answering Benchmark}}, | |
year = {2023}, | |
eprint = {2309.16511}, | |
eprinttype = {arxiv}, | |
eprintclass = {cs.CV}, | |
language = {english}, | |
} | |
``` | |
### Supported Tasks and Leaderboards | |
Grounding Visual Question Answering | |
### Language | |
English | |
## Dataset Structure | |
### Data Instances | |
A data instance contains a URL to the picture, information about the image size - width and height, information about the ground truth bounding box - left top and right bottom dots, and contains the question related to the picture. | |
``` | |
{'image': https://toloka-cdn.azureedge.net/wsdmcup2023/000000000013.jpg, | |
'width': 640, | |
'height': 427, | |
'left': 129, | |
'top': 192, | |
'right': 155, | |
'bottom': 212, | |
'question': What does it use to breath?} | |
``` | |
### Data Fields | |
* image: contains URL to the image | |
* width: value in pixels of image width | |
* height: value in pixels of image height | |
* left: the x coordinate in pixels to determine the left-top dot of the bounding box | |
* top: the y coordinate in pixels to determine the left-top dot of the bounding box | |
* right: the x coordinate in pixels to determine the right-bottom dot of the bounding box | |
* bottom: the y coordinate in pixels to determine the right-bottom dot of the bounding box | |
* question: a question related to the picture | |
### Data Splits | |
There are four splits in the data: train, train_sample, test_public, and test_private. 'train' split contains the full pull for model training. | |
The 'train-sample' split contains the part of the 'train' split. The 'test_public' split contains public data to test the model. | |
The 'test_private' split contains private data for the final model test. | |
### Source Data | |
The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in | |
Context dataset, [MS COCO](https://cocodataset.org/). | |
### Annotations | |
All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/). | |
Only annotators who self-reported the knowledge of English had access to the annotation task. | |
### Citation Information | |
* Competition: https://toloka.ai/challenges/wsdm2023 | |
* CodaLab: https://codalab.lisn.upsaclay.fr/competitions/7434 | |
* Dataset: https://doi.org/10.5281/zenodo.7057740 |