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.
EasyPortrait dataset size is about 26GB, and it contains 20 000 RGB images with high quality annotated masks. This dataset is divided into training set, validation set and test set by hashed subject user_id. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.
Training images were received from 5,947 unique users, while validation was from 860 and testing was from 1,570. On average, each EasyPortrait image has 254 polygon points, from which it can be concluded that the annotation is of high quality. Segmentation masks were created from polygons for each annotation.
Annotations are presented as 2D-arrays, images in *.png format with several classes:
Index | Class |
---|---|
0 | BACKGROUND |
1 | PERSON |
2 | SKIN |
3 | LEFT BROW |
4 | RIGHT_BROW |
5 | LEFT_EYE |
6 | RIGHT_EYE |
7 | LIPS |
8 | TEETH |
Also, we provide some additional meta-information for dataset in annotations/meta.zip file:
attachment_id | user_id | data_hash | width | height | brightness | train | test | valid | |
---|---|---|---|---|---|---|---|---|---|
0 | de81cc1c-... | 1b... | e8f... | 1440 | 1920 | 136 | True | False | False |
1 | 3c0cec5a-... | 64... | df5... | 1440 | 1920 | 148 | False | False | True |
2 | d17ca986-... | cf... | a69... | 1920 | 1080 | 140 | False | True | False |
where: - attachment_id - image file name without extension - user_id - unique anonymized user ID - data_hash - image hash by using Perceptual hashing - width - image width - height - image height - brightness - image brightness - train, test, valid are the binary columns for train / test / val subsets respectively
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Dive into the Live Streamer Portrait Segmentation Dataset Crucial for stream enhancements, AI innovations, and video tech research.
https://maadaa.ai/path/to/licensehttps://maadaa.ai/path/to/license
The "Live Streamer Portrait Segmentation Dataset" is tailored for the media and entertainment industry, consisting of screenshots from live streams with resolution sizes ranging from 540 x 960 to 720 x 1280 pixels. This dataset focuses on contour segmentation, distinguishing between the human body (specifically the live streamer) and the background, aiming to enhance content personalization and viewer engagement in live streaming platforms.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Our Eastern Asia Single-person Portrait Matting Dataset targets the nuanced requirements of the fashion, internet, and entertainment sectors, featuring single-person portraits from Eastern Asia in a variety of settings including indoor, outdoor, street, and sport. This dataset is specially curated for pixel-level fine segmentation tasks, capturing diverse postures and scenarios.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Explore EasyPortrait, a 26GB dataset with 20,000 high-quality RGB images and annotated masks for advanced portrait segmentation and face parsing.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
A large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K frames. This dataset contains various teleconferencing scenes, various actions of the participants, interference of passers-by and illumination change.
The market is segmented by service type into event photography, portrait photography, commercial photography and others. Event photography emerges as the dominant service type segmentation in the Indian photographic services market, capturing a significant share of the market. This dominance is primarily driven by the multitude of social, corporate, and cultural events hosted across the country. By Service Type: The Photographic services market is segmented by client type into individuals & Families, Businesses and others. Individuals and families constitute the dominant client type segmentation in the Indian photographic services market, commanding a significant approximately half share. This dominance is primarily driven by the growing demand for personal photography services, including portrait sessions, family photoshoots, and individual portraits. By Client Type: The Indian photographic services market is segmented based on client type, service type, and region. Here's a breakdown of three key segmentation categories: India Photographic Services Market Segmentation Canon India Pvt. Ltd. is the dominant player due to its extensive range of high-quality cameras and accessories, strong brand reputation, and comprehensive after-sales service.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
A Segmentation Full Body MADS Dataset with 1192 images typically refers to a collection of images that have been annotated for the purpose of full-body segmentation..
https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order
The AI Portrait Generator market is rapidly evolving, driven by advancements in artificial intelligence and machine learning technologies. This innovative segment leverages deep learning algorithms to create stunning, lifelike portraits from simple inputs, opening new avenues for artists, businesses, and individuals
A hair wisp dataset for instance segmentation, consisting of real portrait photos and ground-truth annotations of hair wisps.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
The Portrait Matting Dataset caters to the apparel and media & entertainment sectors, featuring a diverse collection of live screenshot images with resolutions varying from 138 × 189 to 6000 × 4000. This dataset is comprehensive, including single individuals, groups, and their accessories, and is annotated for contour, semantic, and instance segmentation tasks.
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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.
EasyPortrait dataset size is about 26GB, and it contains 20 000 RGB images with high quality annotated masks. This dataset is divided into training set, validation set and test set by hashed subject user_id. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.
Training images were received from 5,947 unique users, while validation was from 860 and testing was from 1,570. On average, each EasyPortrait image has 254 polygon points, from which it can be concluded that the annotation is of high quality. Segmentation masks were created from polygons for each annotation.
Annotations are presented as 2D-arrays, images in *.png format with several classes:
Index | Class |
---|---|
0 | BACKGROUND |
1 | PERSON |
2 | SKIN |
3 | LEFT BROW |
4 | RIGHT_BROW |
5 | LEFT_EYE |
6 | RIGHT_EYE |
7 | LIPS |
8 | TEETH |
Also, we provide some additional meta-information for dataset in annotations/meta.zip file:
attachment_id | user_id | data_hash | width | height | brightness | train | test | valid | |
---|---|---|---|---|---|---|---|---|---|
0 | de81cc1c-... | 1b... | e8f... | 1440 | 1920 | 136 | True | False | False |
1 | 3c0cec5a-... | 64... | df5... | 1440 | 1920 | 148 | False | False | True |
2 | d17ca986-... | cf... | a69... | 1920 | 1080 | 140 | False | True | False |
where: - attachment_id - image file name without extension - user_id - unique anonymized user ID - data_hash - image hash by using Perceptual hashing - width - image width - height - image height - brightness - image brightness - train, test, valid are the binary columns for train / test / val subsets respectively