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11 datasets found
  1. P

    EasyPortrait Dataset

    • paperswithcode.com
    Updated Apr 25, 2023
    + more versions
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    Karina Kvanchiani; Elizaveta Petrova; Karen Efremyan; Alexander Sautin; Alexander Kapitanov (2023). EasyPortrait Dataset [Dataset]. https://paperswithcode.com/dataset/easyportrait
    Explore at:
    Dataset updated
    Apr 25, 2023
    Authors
    Karina Kvanchiani; Elizaveta Petrova; Karen Efremyan; Alexander Sautin; Alexander Kapitanov
    Description

    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:

    IndexClass
    0BACKGROUND
    1PERSON
    2SKIN
    3LEFT BROW
    4RIGHT_BROW
    5LEFT_EYE
    6RIGHT_EYE
    7LIPS
    8TEETH

    Also, we provide some additional meta-information for dataset in annotations/meta.zip file:

    attachment_iduser_iddata_hashwidthheightbrightnesstraintestvalid
    0de81cc1c-...1b...e8f...14401920136TrueFalseFalse
    13c0cec5a-...64...df5...14401920148FalseFalseTrue
    2d17ca986-...cf...a69...19201080140FalseTrueFalse

    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

  2. g

    Live Streamer Portrait Segmentation Dataset

    • gts.ai
    json
    Updated Nov 8, 2022
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    GTS (2022). Live Streamer Portrait Segmentation Dataset [Dataset]. https://gts.ai/case-study/live-streamer-portrait-segmentation-dataset/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Nov 8, 2022
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Dive into the Live Streamer Portrait Segmentation Dataset Crucial for stream enhancements, AI innovations, and video tech research.

  3. M

    Live Streamer Portrait Segmentation Dataset

    • maadaa.ai
    image
    Updated Mar 7, 2024
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    Maadaa AI (2024). Live Streamer Portrait Segmentation Dataset [Dataset]. https://maadaa.ai/datasets/DatasetsDetail/Live-Streamer-Portrait-Segmentation-Dataset
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    imageAvailable download formats
    Dataset updated
    Mar 7, 2024
    Dataset authored and provided by
    Maadaa AI
    License

    https://maadaa.ai/path/to/licensehttps://maadaa.ai/path/to/license

    Variables measured
    Human Body
    Measurement technique
    Contour segmentation
    Description

    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.

  4. s

    Eastern Asia Single-person Portrait Matting Dataset

    • mt.shaip.com
    • maadaa.ai
    • +63more
    json
    Updated Feb 17, 2025
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    Shaip (2025). Eastern Asia Single-person Portrait Matting Dataset [Dataset]. https://mt.shaip.com/offerings/facial-body-part-segmentation-and-recognition-datasets/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Feb 17, 2025
    Dataset authored and provided by
    Shaip
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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.

  5. g

    EasyPortrait

    • gts.ai
    json
    Updated Jun 21, 2024
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    GTS (2024). EasyPortrait [Dataset]. https://gts.ai/dataset-download/easyportrait/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 21, 2024
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Explore EasyPortrait, a 26GB dataset with 20,000 high-quality RGB images and annotated masks for advanced portrait segmentation and face parsing.

  6. O

    PP-HumanSeg14K

    • opendatalab.com
    • paperswithcode.com
    zip
    Updated Jan 20, 2022
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    Baidu (2022). PP-HumanSeg14K [Dataset]. https://opendatalab.com/OpenDataLab/PP-HumanSeg14K
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    zipAvailable download formats
    Dataset updated
    Jan 20, 2022
    Dataset provided by
    Baidu
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    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.

  7. k

    Indian Photographic Services Market Segmentation by Service Type

    • kenresearch.com
    Updated Jun 24, 2024
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    Ken Research (2024). Indian Photographic Services Market Segmentation by Service Type [Dataset]. https://www.kenresearch.com/industry-reports/indian-photographic-services-market
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    Dataset updated
    Jun 24, 2024
    Dataset authored and provided by
    Ken Research
    Description

    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.

  8. g

    Segmentation Full Body MADS Dataset, 1192 images.

    • gts.ai
    json
    Updated Nov 20, 2023
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    GTS (2023). Segmentation Full Body MADS Dataset, 1192 images. [Dataset]. https://gts.ai/dataset-download/segmentation-full-body-mads-dataset-1192-images/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Nov 20, 2023
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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..

  9. I

    Global AI Portrait Generator Market Growth Opportunities 2025-2032

    • statsndata.org
    excel, pdf
    Updated Feb 2025
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    Stats N Data (2025). Global AI Portrait Generator Market Growth Opportunities 2025-2032 [Dataset]. https://www.statsndata.org/report/ai-portrait-generator-market-337710
    Explore at:
    pdf, excelAvailable download formats
    Dataset updated
    Feb 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    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

  10. t

    Hair-Wisp dataset

    • service.tib.eu
    Updated Dec 3, 2024
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    (2024). Hair-Wisp dataset [Dataset]. https://service.tib.eu/ldmservice/dataset/hair-wisp-dataset
    Explore at:
    Dataset updated
    Dec 3, 2024
    Description

    A hair wisp dataset for instance segmentation, consisting of real portrait photos and ground-truth annotations of hair wisps.

  11. s

    Portrait Matting Dataset

    • hmn.shaip.com
    json
    Updated Dec 7, 2024
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    Shaip (2024). Portrait Matting Dataset [Dataset]. https://hmn.shaip.com/offerings/facial-body-part-segmentation-and-recognition-datasets/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Dec 7, 2024
    Dataset authored and provided by
    Shaip
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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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    Learn how you can add new datasets to our index.

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Karina Kvanchiani; Elizaveta Petrova; Karen Efremyan; Alexander Sautin; Alexander Kapitanov (2023). EasyPortrait Dataset [Dataset]. https://paperswithcode.com/dataset/easyportrait

EasyPortrait Dataset

Face Parsing and Portrait Segmentation Dataset

Explore at:
5 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 25, 2023
Authors
Karina Kvanchiani; Elizaveta Petrova; Karen Efremyan; Alexander Sautin; Alexander Kapitanov
Description

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:

IndexClass
0BACKGROUND
1PERSON
2SKIN
3LEFT BROW
4RIGHT_BROW
5LEFT_EYE
6RIGHT_EYE
7LIPS
8TEETH

Also, we provide some additional meta-information for dataset in annotations/meta.zip file:

attachment_iduser_iddata_hashwidthheightbrightnesstraintestvalid
0de81cc1c-...1b...e8f...14401920136TrueFalseFalse
13c0cec5a-...64...df5...14401920148FalseFalseTrue
2d17ca986-...cf...a69...19201080140FalseTrueFalse

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