| Name | findingemo-light JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | FindingEmo-Light: Data only. |
| upload_time | 2024-12-04 20:19:34 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
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| keywords |
ann
neural networks
emotion recognition
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# FindingEmo-Light — Dataset Only
<div align="center">
<hr>
🚨⚠️🚨 NOT FOR COMMERCIAL PURPOSES 🚨⚠️🚨
<hr>
</div>
This repository contains only the annotations and URL list of the FindingEmo dataset, reported in the paper
["FindingEmo: A Picture Dataset for Emotion Recognition in the Wild"](https://arxiv.org/abs/2402.01355) by Laurent Mertens et al.,
accepted at the [NeurIPS 2024 Datasets and Benchmarks Track](https://nips.cc/virtual/2024/poster/97871).
If you also require the code, please visit [https://gitlab.com/EAVISE/lme/findingemo](https://gitlab.com/EAVISE/lme/findingemo) instead.
## <a name="obtaining_images"></a>Obtaining the annotated images
### Solution 1: Using the PyPi package
Install the FindingEmo-Light package:
```
pip install findingemo-light
```
Then, in a Python script, do
```python
from findingemo_light.paper.download_multi import download_data
download_data(target_dir='./Path/To/Where/You/Want/To/Download/The/Images')
```
To get the annotations, use
```python
from findingemo_light.data.read_annotations import read_annotations
ann_data = read_annotations()
print(ann_data)
```
### Solution 2: Using the codebase
Clone the repository locally, execute ```paper/download_multi.py``` and save images to ```Config.DIR_IMAGES```
(which is the default save location).
The annotations are located under ```data/annotations_single.ann```.
## Logo
The ```./data/Logo``` folder contains image files with the dataset logo in various formats. If you find this data useful,
feel free to use the logo on your poster.
<div align="center">
<img src="https://www.laurentmertens.com/FindingEmo/FindingEmo_Color_Logo.png">
</div>
## Dataset
The <u><b>annotations</b></u> are stored in the [```data/annotations_single.ann```](findingemo_light/data/annotations_single.ann) file, which is a simple text file in CSV format.
The [```data/dataset_urls_exploded.json```](findingemo_light/data/dataset_urls_exploded.json) file contains the <u><b>URLs for the annotated images</b></u>, with multiple URLs provided
for a large number of images. We intend to update this file as we obtain backup URLs for more images.
A <u><b>Croissant metadata</b></u> file is also included ([```./croissant-findingemo.json```](./croissant-findingemo.json) to allow loading the dataset through the [Croissant](https://github.com/mlcommons/croissant) framework.
Dataset <u><b>documentation</b></u> can be found at [```datasheet/datasheet.md```](findingemo_light/datasheet/datasheet.md).
## Legal Compliance and Privacy
This dataset contains URLs to potentially copyrighted material. If you are a member of a research institution located
within the European Union, you are allowed to use this material for **non-commercial research purposes** by virtue of Title
II, Article 3 of the [InfSoc directive](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32019L0790)). If you
are not located within the European Union, it is your responsibility to verify with local legislation whether you are
allowed to use this material or not.
<font color='red'>In case you are the legal copyright holder of any of the images we provide a link to, or are depicted in any of these
images, and you do not wish that your material and/or likeness be used for Machine Learning purposes, you can contact
either [laurent.mertens@kuleuven.be](laurent.mertens@kuleuven.be) or
[joost.vennekens@kuleuven.be](joost.vennekens@kuleuven.be) with the details of the image, and we will immediately remove
it from the dataset.
**IN NO CASE SHALL THIS DATASET BE USED FOR ANY COMMERCIAL PURPOSE.**</font>
## Licensing
All data (annotations + list of URLs) is shared under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa] (see [LICENSE_data.md](./LICENSE_data.md)).
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
## Data overview
The ```data``` folder contains the CSV/TXT files containing the results per experiment used to generate the results
reported in the paper.
The ```data/Logo``` folder contains image files with the dataset logo.
The ```annotations_single.ann``` file, which is a simple text file in CSV format, contains the image annotations.
The ```dataset_urls_exploded.json``` file is the extended version of ```dataset_urls.txt```, which list multiple URLs for a
number of images, and is used by the ```paper/download_multi.py``` script.
## Acknowledgment
This work was funded by KU Leuven grant IDN/21/010.
Author: Laurent Mertens\
Mail: [laurent.mertens@kuleuven.be](laurent.mertens@kuleuven.be)
Raw data
{
"_id": null,
"home_page": null,
"name": "findingemo-light",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "Laurent Mertens <laurent.mertens@kuleuven.be>",
"keywords": "ANN, Neural Networks, Emotion Recognition",
"author": null,
"author_email": "Laurent Mertens <laurent.mertens@kuleuven.be>",
"download_url": "https://files.pythonhosted.org/packages/86/32/b25eee185832b95d8d3107591460771dd87d0f46e52536e80a1df074133c/findingemo_light-1.0.0.tar.gz",
"platform": null,
"description": "# FindingEmo-Light \u2014 Dataset Only\n\n<div align=\"center\">\n<hr>\n\ud83d\udea8\u26a0\ufe0f\ud83d\udea8 NOT FOR COMMERCIAL PURPOSES \ud83d\udea8\u26a0\ufe0f\ud83d\udea8\n<hr>\n</div>\n\nThis repository contains only the annotations and URL list of the FindingEmo dataset, reported in the paper\n[\"FindingEmo: A Picture Dataset for Emotion Recognition in the Wild\"](https://arxiv.org/abs/2402.01355) by Laurent Mertens et al.,\naccepted at the [NeurIPS 2024 Datasets and Benchmarks Track](https://nips.cc/virtual/2024/poster/97871).\n\nIf you also require the code, please visit [https://gitlab.com/EAVISE/lme/findingemo](https://gitlab.com/EAVISE/lme/findingemo) instead.\n\n## <a name=\"obtaining_images\"></a>Obtaining the annotated images\n### Solution 1: Using the PyPi package\nInstall the FindingEmo-Light package:\n```\npip install findingemo-light\n```\nThen, in a Python script, do\n```python\nfrom findingemo_light.paper.download_multi import download_data\n\ndownload_data(target_dir='./Path/To/Where/You/Want/To/Download/The/Images')\n```\nTo get the annotations, use\n```python\nfrom findingemo_light.data.read_annotations import read_annotations\n\nann_data = read_annotations()\nprint(ann_data)\n```\n\n### Solution 2: Using the codebase\nClone the repository locally, execute ```paper/download_multi.py``` and save images to ```Config.DIR_IMAGES```\n(which is the default save location).\n\nThe annotations are located under ```data/annotations_single.ann```.\n\n## Logo\nThe ```./data/Logo``` folder contains image files with the dataset logo in various formats. If you find this data useful,\nfeel free to use the logo on your poster.\n\n<div align=\"center\">\n<img src=\"https://www.laurentmertens.com/FindingEmo/FindingEmo_Color_Logo.png\">\n</div>\n\n## Dataset\nThe <u><b>annotations</b></u> are stored in the [```data/annotations_single.ann```](findingemo_light/data/annotations_single.ann) file, which is a simple text file in CSV format.\nThe [```data/dataset_urls_exploded.json```](findingemo_light/data/dataset_urls_exploded.json) file contains the <u><b>URLs for the annotated images</b></u>, with multiple URLs provided\nfor a large number of images. We intend to update this file as we obtain backup URLs for more images.\n\nA <u><b>Croissant metadata</b></u> file is also included ([```./croissant-findingemo.json```](./croissant-findingemo.json) to allow loading the dataset through the [Croissant](https://github.com/mlcommons/croissant) framework.\n\nDataset <u><b>documentation</b></u> can be found at [```datasheet/datasheet.md```](findingemo_light/datasheet/datasheet.md).\n\n## Legal Compliance and Privacy\nThis dataset contains URLs to potentially copyrighted material. If you are a member of a research institution located\nwithin the European Union, you are allowed to use this material for **non-commercial research purposes** by virtue of Title\nII, Article 3 of the [InfSoc directive](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32019L0790)). If you\nare not located within the European Union, it is your responsibility to verify with local legislation whether you are\nallowed to use this material or not.\n\n<font color='red'>In case you are the legal copyright holder of any of the images we provide a link to, or are depicted in any of these\nimages, and you do not wish that your material and/or likeness be used for Machine Learning purposes, you can contact\neither [laurent.mertens@kuleuven.be](laurent.mertens@kuleuven.be) or\n[joost.vennekens@kuleuven.be](joost.vennekens@kuleuven.be) with the details of the image, and we will immediately remove\nit from the dataset.\n\n**IN NO CASE SHALL THIS DATASET BE USED FOR ANY COMMERCIAL PURPOSE.**</font>\n\n## Licensing\nAll data (annotations + list of URLs) is shared under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa] (see [LICENSE_data.md](./LICENSE_data.md)).\n\n[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/\n[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg\n\n## Data overview\nThe ```data``` folder contains the CSV/TXT files containing the results per experiment used to generate the results\nreported in the paper.\n\nThe ```data/Logo``` folder contains image files with the dataset logo.\n\nThe ```annotations_single.ann``` file, which is a simple text file in CSV format, contains the image annotations.\n\nThe ```dataset_urls_exploded.json``` file is the extended version of ```dataset_urls.txt```, which list multiple URLs for a\nnumber of images, and is used by the ```paper/download_multi.py``` script.\n\n## Acknowledgment\nThis work was funded by KU Leuven grant IDN/21/010.\n\n\nAuthor: Laurent Mertens\\\nMail: [laurent.mertens@kuleuven.be](laurent.mertens@kuleuven.be)\n",
"bugtrack_url": null,
"license": "Attribution-NonCommercial-ShareAlike 4.0 International ======================================================================= Creative Commons Corporation (\"Creative Commons\") is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an \"as-is\" basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. Using Creative Commons Public Licenses Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC- licensed material, or material used under an exception or limitation to copyright. More considerations for licensors: wiki.creativecommons.org/Considerations_for_licensors Considerations for the public: By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor's permission is not necessary for any reason--for example, because of any applicable exception or limitation to copyright--then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. 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