# 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](http://SciCap.AI) 🏠*

## 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](https://aclanthology.org/2021.findings-emnlp.277.pdf) 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](https://arxiv.org/pdf/2301.12293.pdf).

You can download the dataset using the following command:

```python
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:

```json
[
  {
    "image_id": int, 
    "caption": "PREDICTED CAPTION STRING"
  },
  {
    "image_id": int,
    "caption": "PREDICTED CAPTION STRING"
  }
...
]
```

Submit your results using this [challenge link](https://eval.ai/web/challenges/challenge-page/2012/overview) 🔗. Participants must register on [Eval.AI](http://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! 🍀🎊
license:cc-by-nc-sa-4.0 arxiv:2301.12293 region:us
{"license": "cc-by-nc-sa-4.0"}
TAGS
#license-cc-by-nc-sa-4.0 #arxiv-2301.12293 #region-us 

# 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:



_Merge all image split files into one_ 


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:



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! 
# 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:



_Merge all image split files into one_ 


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:



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!
TAGS
#license-cc-by-nc-sa-4.0 #arxiv-2301.12293 #region-us 
 # 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:



_Merge all image split files into one_ 


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:



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!