People Clothing Segmentation

### Context

Semantic Segmentation is one of major tasks in Computer Vision. It is the pixel-wise classification of an image into object classes. This dataset contains 1000 images and segmentation masks pairs of individual people's clothing. With 59 object classes and a relatively lesser data, the task of modelling is expected to be a challenging one! The data needs no preprocessing, all images are of same size, same format, and ready to model.

### Content

The dataset contains 1000 images and 1000 corresponding semantic segmentation masks each of size 825 pixels by 550 pixels in PNG format. The segmentation masks belong to 59 classes, the first being the background of individuals, and the rest belong to 58 clothing classes such as shirt, hair, pants, skin, shoes, glasses and so on. A CSV file containing the list of 59 classes is included in the dataset. The dataset contains data in both JPEG formats and PNG formats. However, JPEG is found to be lossy, while PNG is lossless with the essence of Originality.

### Acknowledgements

The raw contents of this dataset are collected as such from the Github Repository: [https://github.com/bearpaw/clothing-co-parsing](https://github.com/bearpaw/clothing-co-parsing). However, this dataset is a transformed version of the original dataset.
The original work can be found at:
`@inproceedings{yang2014clothing,
title={Clothing Co-Parsing by Joint Image Segmentation and Labeling},
author={Yang, Wei and Luo, Ping and Lin, Liang}
booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
year={2013},
organization={IEEE}
}
`

### Inspiration

The original dataset acknowledged above has images of different sizes and different formats such as JPG and MATLAB. This kernel [https://www.kaggle.com/rajkumarl/how-to-convert-matlab-file-into-png-image-file](https://www.kaggle.com/rajkumarl/how-to-convert-matlab-file-into-png-image-file) processed the above mentioned original dataset and transformed every data into a ready-to-use format of uniform size. No image or semantic segmentation mask available in this dataset is a copy of original dataset. This dataset is a completely transformed version, prepared to help Computer Vision people concentrate on their modeling part, rather than spend time preprocessing the images and masks.

{'Context': '语义分割是计算机视觉领域的重要任务之一，它涉及对图像进行像素级的类别分类。本数据集包含1000张图像及其对应的分割掩码对，涉及个人服饰类别。由于包含59个类别且数据量相对较少，建模任务预计将极具挑战性！数据集无需预处理，所有图像均具有相同的大小和格式，可直接用于模型训练。', 'Content': '本数据集包含1000张图像及其对应的语义分割掩码，每张图像和掩码均为825像素×550像素，并以PNG格式存储。分割掩码分为59个类别，首类为个人背景，其余58类为服饰类别，例如衬衫、头发、裤子、皮肤、鞋子、眼镜等。数据集中还包含一份包含59个类别的CSV文件。数据集包含JPEG和PNG格式的图像数据。然而，JPEG格式为有损格式，而PNG格式为无损格式，保留了原始数据的真实性。', 'Acknowledgements': '本数据集的原始内容收集自Github仓库：[https://github.com/bearpaw/clothing-co-parsing](https://github.com/bearpaw/clothing-co-parsing)。然而，本数据集是对原始数据集的转换版本。原始研究工作可参考以下文献：
`@inproceedings{yang2014clothing,
title={Clothing Co-Parsing by Joint Image Segmentation and Labeling},
author={Yang, Wei and Luo, Ping and Lin, Liang},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
year={2013},
organization={IEEE}
}`', 'Inspiration': '上述提到的原始数据集包含不同尺寸和不同格式的图像，如JPG和MATLAB。该数据集的转换得益于以下Kaggle核函数的处理：[https://www.kaggle.com/rajkumarl/how-to-convert-matlab-file-into-png-image-file](https://www.kaggle.com/rajkumarl/how-to-convert-matlab-file-into-png-image-file)。该核函数将原始数据集中的数据转换成了统一格式的可使用数据。本数据集中的任何图像或语义分割掩码均非原始数据集的复制。本数据集是一个完全转换后的版本，旨在帮助计算机视觉研究者集中精力进行模型训练，而非花费时间在图像和掩码的预处理上。'}