Datasets:
The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ValueError
Message: Not able to read records in the JSON file at hf://datasets/CrowdAILab/scicap@60e504baa94423f63cda87d5442e73a696b953d3/train-acl.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['images', 'annotations']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at hf://datasets/CrowdAILab/scicap@60e504baa94423f63cda87d5442e73a696b953d3/train-acl.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['images', 'annotations']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
The 1st Scientific Figure Captioning (SciCap) Challenge 📖📊
Welcome to the 1st Scientific Figure Captioning (SciCap) Challenge! 🎉 This dataset contains approximately 400,000 scientific figure images sourced from various arXiv papers, along with their captions and relevant paragraphs. The challenge is open to researchers, AI/NLP/CV practitioners, and anyone interested in developing computational models for generating textual descriptions for visuals. 💻
Challenge homepage 🏠
Challenge Overview 🌟
The SciCap Challenge will be hosted at ICCV 2023 in the 5th Workshop on Closing the Loop Between Vision and Language (October 2-3, Paris, France) 🇫🇷. Participants are required to submit the generated captions for a hidden test set for evaluation.
The challenge is divided into two phases:
- Test Phase (2.5 months): Use the provided training set, validation set, and public test set to build and test the models.
- Challenge Phase (2 weeks): Submit results for a hidden test set that will be released before the submission deadline.
Winning teams will be determined based on their results for the hidden test set 🏆. Details of the event's important dates, prizes, and judging criteria are listed on the challenge homepage.
Dataset Overview and Download 📚
The SciCap dataset contains an expanded version of the original SciCap dataset, and includes figures and captions from arXiv papers in eight categories: Computer Science, Economics, Electrical Engineering and Systems Science, Mathematics, Physics, Quantitative Biology, Quantitative Finance, and Statistics 📊. Additionally, it covers data from ACL Anthology papers ACL-Fig.
You can download the dataset using the following command:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="CrowdAILab/scicap", repo_type='dataset')
Merge all image split files into one 🧩
zip -F img-split.zip --out img.zip
The dataset schema is similar to the mscoco dataset:
- images: two separated folders - arXiv and acl figures 📁
- annotations: JSON files containing text information (filename, image id, figure type, OCR, and mapped image id, captions, normalized captions, paragraphs, and mentions) 📝
Evaluation and Submission 📩
You have to submit your generated captions in JSON format as shown below:
[
{
"image_id": int,
"caption": "PREDICTED CAPTION STRING"
},
{
"image_id": int,
"caption": "PREDICTED CAPTION STRING"
}
...
]
Submit your results using this challenge link 🔗. Participants must register on Eval.AI to access the leaderboard and submit results.
Please note: Participants should not use the original captions from the arXiv papers (termed "gold data") as input for their systems ⚠️.
Technical Report Submission 🗒️
All participating teams must submit a 2-4 page technical report detailing their system, adhering to the ICCV 2023 paper template 📄. Teams have the option to submit their reports to either the archival or non-archival tracks of the 5th Workshop on Closing the Loop Between Vision and Language.
Good luck with your participation in the 1st SciCap Challenge! 🍀🎊
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