Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Sub-tasks:
semantic-segmentation
Size:
10K<n<100K
ArXiv:
License:
gofixyourself
commited on
Commit
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ed03139
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Parent(s):
8848b11
Removed invalid tasks names
Browse files
README.md
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@@ -4,8 +4,6 @@ task_categories:
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- image-segmentation
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task_ids:
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- semantic-segmentation
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- portrait-segmentation
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- face-parsing
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size_categories:
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- 10K<n<100K
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annotations_creators:
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# EasyPortrait - Face Parsing and Portrait Segmentation Dataset
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We introduce a large-scale image dataset **EasyPortrait** for portrait segmentation and face parsing. Proposed dataset can be used in several tasks, such as background removal in conference applications, teeth whitening, face skin enhancement, red eye removal or eye colorization, and so on.
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EasyPortrait dataset size is about **26GB**, and it contains **20 000** RGB images (~17.5K FullHD images) with high quality annotated masks. This dataset is divided into training set, validation set and test set by subject `user_id`. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.
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For more information see our paper [EasyPortrait – Face Parsing and Portrait Segmentation Dataset](https://arxiv.org/abs/2304.13509).
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## Structure
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```
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- `width` - image width
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- `height` - image height
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- `brightness` - image brightness
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- `train`, `test`, `valid` are the binary columns for train / test / val subsets respectively
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## Authors and Credits
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- [Alexander Kapitanov](https://www.linkedin.com/in/hukenovs)
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- image-segmentation
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task_ids:
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- semantic-segmentation
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size_categories:
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- 10K<n<100K
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annotations_creators:
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# EasyPortrait - Face Parsing and Portrait Segmentation Dataset
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![easyportrait](support_images/main.jpg)
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We introduce a large-scale image dataset **EasyPortrait** for portrait segmentation and face parsing. Proposed dataset can be used in several tasks, such as background removal in conference applications, teeth whitening, face skin enhancement, red eye removal or eye colorization, and so on.
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EasyPortrait dataset size is about **26GB**, and it contains **20 000** RGB images (~17.5K FullHD images) with high quality annotated masks. This dataset is divided into training set, validation set and test set by subject `user_id`. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.
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For more information see our paper [EasyPortrait – Face Parsing and Portrait Segmentation Dataset](https://arxiv.org/abs/2304.13509).
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## The model results trained on the EasyPortrait dataset
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## Structure
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```
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.
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- `width` - image width
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- `height` - image height
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- `brightness` - image brightness
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- `train`, `test`, `valid` are the binary columns for train / test / val subsets respectively
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## Authors and Credits
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- [Alexander Kapitanov](https://www.linkedin.com/in/hukenovs)
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