| Name | regsets JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | A collection of regression datasets with PyTorch-like dataset classes. |
| upload_time | 2024-10-17 10:18:47 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2024 Pin-Yen Huang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
regression
dataset
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
librosa
numpy
pandas
Pillow
torch
torchvision
tqdm
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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<h1 align="center">🎛️ Regression Datasets</h1>
<!-- TABLE OF CONTENTS -->
<details>
<summary><strong>📋 Table of Contents</strong></summary>
<ol>
<li><a href="#1-installation">Installation</a></li>
<li><a href="#2-usage">Usage</a></li>
<li><a href="#3-datasets">Datasets</a></li>
<li><a href="#4-license">License</a></li>
<li><a href="#5-contact">Contact</a></li>
<li><a href="#6-acknowledgments">Acknowledgments</a></li>
</ol>
</details>
This repository offers a diverse collection of regression datasets across vision, audio and text domains. It provides dataset classes that follow the <a href="https://github.com/pytorch/vision/tree/main/torchvision/datasets">PyTorch Datasets</a> structure, allowing users to automatically download and load these datasets with ease. All datasets come with a permissive license, permitting their use for research purposes.
<!-- Installation -->
## 1. Installation
To install the `regsets` package, you can use pip:
```sh
python -m pip install regsets
```
Alternatively, you can download a specific dataset file (e.g., `utkface.py`) and include it in your project to load the dataset locally.
<!-- USAGE -->
## 2. Usage
Below are examples of how to use the `regsets` package for loading datasets.
### 📸 Vision Datasets
```python
from regsets.vision import UTKFace
utkface_trainset = UTKFace(root="./data", split="train", download=True)
for image, label in utkface_trainset:
...
```
### 🎧 Audio Datasets
```python
from regsets.audio import VCC2018
vcc2018_trainset = VCC2018(root="./data", split="train", download=True)
for audio, sample_rate, label in vcc2018_trainset:
...
```
### 📝 Text Datasets
```python
from regsets.text import Amazon_Review
amazon_review_trainset = Amazon_Review(root="./data", split="train", download=True)
for texts, label in amazon_review_trainset:
(ori, aug_0, aug_1) = texts
...
```
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- DATASETS -->
## 3. Datasets
For datasets that do not provide a predefined train-test split, I randomly sample 80% of the data for training and reserve the remaining 20% for testing. Details for each dataset are provided below.
### 📸 Vision Datasets
| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |
| ------- | --------------- | ---------- | ----------- | ------------ |
| UTKFace | 18,964 | - | 4,741 | [1, 116] |
### 🎧 Audio Datasets
| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |
| ------- | --------------- | ---------- | ----------- | ------------ |
| BVCC | 4,974 | 1,066 | 1,066 | [1, 5] |
| VCC2018 | 16,464 | - | 4,116 | [1, 5] |
### 📝 Text Datasets
| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |
| ------------- | --------------- | ---------- | ----------- | ------------ |
| Amazon Review | 250,000 | 25,000 | 650,000 | [0, 4] |
| Yelp Review | 250,000 | 25,000 | 50,000 | [0, 4] |
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- LICENSE -->
## 4. License
Distributed under the MIT License. See [LICENSE](LICENSE) for more information.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTACT -->
## 5. Contact
- Pin-Yen Huang (pyhuang97@gmail.com)
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- ACKNOWLEDGMENTS -->
## 6. Acknowledgments
- [PyTorch](https://github.com/pytorch)
- [UTKFace](https://susanqq.github.io/UTKFace)
- [VCC2018](https://datashare.ed.ac.uk/handle/10283/3061)
- [BVCC](https://zenodo.org/records/6572573)
- [USB](https://github.com/microsoft/semi-supervised-learning)
- [Amazon Review](https://dl.acm.org/doi/10.1145/2507157.2507163)
- [Yelp Review](http://www.yelp.com/dataset_challenge)
- [README Template](https://github.com/othneildrew/Best-README-Template)
<p align="right">(<a href="#readme-top">back to top</a>)</p>
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"description": "<!-- Improved compatibility of back to top link: See: https://github.com/othneildrew/Best-README-Template/pull/73 -->\n\n<a id=\"readme-top\"></a>\n\n<!--\n*** Thanks for checking out the Best-README-Template. If you have a suggestion\n*** that would make this better, please fork the repo and create a pull request\n*** or simply open an issue with the tag \"enhancement\".\n*** Don't forget to give the project a star!\n*** Thanks again! Now go create something AMAZING! :D\n-->\n\n<h1 align=\"center\">\ud83c\udf9b\ufe0f Regression Datasets</h1>\n\n<!-- TABLE OF CONTENTS -->\n<details>\n <summary><strong>\ud83d\udccb Table of Contents</strong></summary>\n <ol>\n <li><a href=\"#1-installation\">Installation</a></li>\n <li><a href=\"#2-usage\">Usage</a></li>\n <li><a href=\"#3-datasets\">Datasets</a></li>\n <li><a href=\"#4-license\">License</a></li>\n <li><a href=\"#5-contact\">Contact</a></li>\n <li><a href=\"#6-acknowledgments\">Acknowledgments</a></li>\n </ol>\n</details>\n\nThis repository offers a diverse collection of regression datasets across vision, audio and text domains. It provides dataset classes that follow the <a href=\"https://github.com/pytorch/vision/tree/main/torchvision/datasets\">PyTorch Datasets</a> structure, allowing users to automatically download and load these datasets with ease. All datasets come with a permissive license, permitting their use for research purposes.\n\n<!-- Installation -->\n\n## 1. Installation\n\nTo install the `regsets` package, you can use pip:\n\n```sh\npython -m pip install regsets\n```\n\nAlternatively, you can download a specific dataset file (e.g., `utkface.py`) and include it in your project to load the dataset locally.\n\n<!-- USAGE -->\n\n## 2. Usage\n\nBelow are examples of how to use the `regsets` package for loading datasets.\n\n### \ud83d\udcf8 Vision Datasets\n\n```python\nfrom regsets.vision import UTKFace\n\nutkface_trainset = UTKFace(root=\"./data\", split=\"train\", download=True)\n\nfor image, label in utkface_trainset:\n ...\n```\n\n### \ud83c\udfa7 Audio Datasets\n\n```python\nfrom regsets.audio import VCC2018\n\nvcc2018_trainset = VCC2018(root=\"./data\", split=\"train\", download=True)\n\nfor audio, sample_rate, label in vcc2018_trainset:\n ...\n```\n\n### \ud83d\udcdd Text Datasets\n\n```python\nfrom regsets.text import Amazon_Review\n\namazon_review_trainset = Amazon_Review(root=\"./data\", split=\"train\", download=True)\n\nfor texts, label in amazon_review_trainset:\n (ori, aug_0, aug_1) = texts\n ...\n```\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- DATASETS -->\n\n## 3. Datasets\n\nFor datasets that do not provide a predefined train-test split, I randomly sample 80% of the data for training and reserve the remaining 20% for testing. Details for each dataset are provided below.\n\n### \ud83d\udcf8 Vision Datasets\n\n| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |\n| ------- | --------------- | ---------- | ----------- | ------------ |\n| UTKFace | 18,964 | - | 4,741 | [1, 116] |\n\n### \ud83c\udfa7 Audio Datasets\n\n| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |\n| ------- | --------------- | ---------- | ----------- | ------------ |\n| BVCC | 4,974 | 1,066 | 1,066 | [1, 5] |\n| VCC2018 | 16,464 | - | 4,116 | [1, 5] |\n\n### \ud83d\udcdd Text Datasets\n\n| Dataset | # Training Data | # Dev Data | # Test Data | Target Range |\n| ------------- | --------------- | ---------- | ----------- | ------------ |\n| Amazon Review | 250,000 | 25,000 | 650,000 | [0, 4] |\n| Yelp Review | 250,000 | 25,000 | 50,000 | [0, 4] |\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- LICENSE -->\n\n## 4. License\n\nDistributed under the MIT License. See [LICENSE](LICENSE) for more information.\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- CONTACT -->\n\n## 5. Contact\n\n- Pin-Yen Huang (pyhuang97@gmail.com)\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- ACKNOWLEDGMENTS -->\n\n## 6. Acknowledgments\n\n- [PyTorch](https://github.com/pytorch)\n- [UTKFace](https://susanqq.github.io/UTKFace)\n- [VCC2018](https://datashare.ed.ac.uk/handle/10283/3061)\n- [BVCC](https://zenodo.org/records/6572573)\n- [USB](https://github.com/microsoft/semi-supervised-learning)\n- [Amazon Review](https://dl.acm.org/doi/10.1145/2507157.2507163)\n- [Yelp Review](http://www.yelp.com/dataset_challenge)\n- [README Template](https://github.com/othneildrew/Best-README-Template)\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n",
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