38 datasets found
  1. h

    FairFace

    • huggingface.co
    • library.toponeai.link
    • +1more
    Updated Mar 2, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    HuggingFaceM4 (2023). FairFace [Dataset]. https://huggingface.co/datasets/HuggingFaceM4/FairFace
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 2, 2023
    Dataset authored and provided by
    HuggingFaceM4
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset Card for FairFace

      Dataset Summary
    

    FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.

      Supported Tasks and Leaderboards
    

    [More Information Needed]

      Languages
    

    [More Information Needed]

      Dataset… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/FairFace.
    
  2. h

    fairface

    • huggingface.co
    Updated Aug 15, 2019
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Nate Raw (2019). fairface [Dataset]. https://huggingface.co/datasets/nateraw/fairface
    Explore at:
    Dataset updated
    Aug 15, 2019
    Authors
    Nate Raw
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset Card for FairFace

      Usage
    

    from io import BytesIO from PIL import Image

    import datasets

    def bytes_to_pil(example_batch): example_batch['img'] = [ Image.open(BytesIO(b)) for b in example_batch.pop('img_bytes') ] return example_batch

    ds = datasets.load_dataset('nateraw/fairface') ds = ds.with_transform(bytes_to_pil)

      Dataset Summary
    

    Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/fairface.

  3. h

    fairface_val_padding_025

    • huggingface.co
    Updated Mar 21, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    nlphuji (2023). fairface_val_padding_025 [Dataset]. https://huggingface.co/datasets/nlphuji/fairface_val_padding_025
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 21, 2023
    Dataset authored and provided by
    nlphuji
    Description

    FairFace (val set)

    Original paper: Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation Homepage: https://github.com/joojs/fairface Bibtex: @inproceedings{karkkainenfairface, title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation}, author={Karkkainen, Kimmo and Joo, Jungseock}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}… See the full description on the dataset page: https://huggingface.co/datasets/nlphuji/fairface_val_padding_025.

  4. h

    m4-bias-eval-fair-face

    • huggingface.co
    Updated Aug 11, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    HuggingFaceM4 (2023). m4-bias-eval-fair-face [Dataset]. https://huggingface.co/datasets/HuggingFaceM4/m4-bias-eval-fair-face
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 11, 2023
    Dataset authored and provided by
    HuggingFaceM4
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset Card for m4-bias-eval-fair-faces

    This dataset consists of generations made by the 80 Billion and 9 Billion variants of the IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) model. IDEFICS is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. In order to evaluate the model's… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/m4-bias-eval-fair-face.

  5. h

    fairface

    • huggingface.co
    Updated Apr 10, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ryan Ramos (2024). fairface [Dataset]. https://huggingface.co/datasets/ryanramos/fairface
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 10, 2024
    Authors
    Ryan Ramos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset Card for FairFace

      Dataset Summary
    

    A dataset of human faces annotated with discrete categories for the photographed person's age, sex, and race. Please consider prioritizing a previously created Hugging Face dataset repository for Fair Face as this new dataset repository was only made for downloading issues that may already be resolved. For complete details on the dataset's construction and intended uses, please refer to the dataset's official repository or paper.… See the full description on the dataset page: https://huggingface.co/datasets/ryanramos/fairface.

  6. FairFace

    • kaggle.com
    Updated Oct 9, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Will (2020). FairFace [Dataset]. https://www.kaggle.com/aibloy/fairface/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 9, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Will
    Description

    Dataset

    This dataset was created by Will

    Contents

  7. h

    fairface

    • huggingface.co
    Updated Feb 27, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Rix Mape (2025). fairface [Dataset]. https://huggingface.co/datasets/rixmape/fairface
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 27, 2025
    Authors
    Rix Mape
    Description

    rixmape/fairface dataset hosted on Hugging Face and contributed by the HF Datasets community

  8. FairFace Small

    • kaggle.com
    Updated Mar 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KESHAV GOEL 123 (2023). FairFace Small [Dataset]. https://www.kaggle.com/datasets/keshavgoel123/fairface-small/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 4, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    KESHAV GOEL 123
    Description

    Dataset

    This dataset was created by KESHAV GOEL 123

    Contents

  9. F

    Fair Face Block Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jan 3, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Data Insights Market (2025). Fair Face Block Report [Dataset]. https://www.datainsightsmarket.com/reports/fair-face-block-238722
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The market for Fair Face Block is poised for steady growth over the forecast period of 2025-2033. Valued at USD 498 million in 2025, the market is expected to reach USD 657.9 million by 2033, exhibiting a CAGR of 3.5%. The increasing demand for aesthetically pleasing and durable building materials, particularly in commercial and residential construction, is primarily driving the market growth. The versatility and sustainability of Fair Face Block, which allows for diverse architectural designs while minimizing environmental impact, further contribute to its popularity. The market is segmented into application, type, and region. Commercial buildings account for the largest share of the market, followed by residential buildings. Low-density blocks dominate the market in terms of type, while North America and Europe are the leading regional markets. Key industry players include Lignacite, O'Reilly Barleystone, Ibstock, Mannok, W&J Chambers, Aggregate Industries, and others. These companies are actively engaged in product innovation, geographical expansion, and strategic partnerships to maintain their market positions and tap into new opportunities.

  10. R

    Fair_face Dataset

    • universe.roboflow.com
    zip
    Updated Mar 14, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    AIFORESTPUBLIC (2025). Fair_face Dataset [Dataset]. https://universe.roboflow.com/aiforestpublic/fair_face
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 14, 2025
    Dataset authored and provided by
    AIFORESTPUBLIC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    People
    Description

    Fair_Face

    ## Overview
    
    Fair_Face is a dataset for classification tasks - it contains People annotations for 14,940 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  11. F

    Fair Face Block Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Mar 24, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Archive Market Research (2025). Fair Face Block Report [Dataset]. https://www.archivemarketresearch.com/reports/fair-face-block-78898
    Explore at:
    ppt, pdf, docAvailable download formats
    Dataset updated
    Mar 24, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global fair face block market, valued at $629 million in 2025, is poised for significant growth. While a precise CAGR isn't provided, considering the construction industry's cyclical nature and increasing demand for aesthetically pleasing and durable building materials, a conservative estimate of a 5% CAGR over the forecast period (2025-2033) is reasonable. This growth is fueled by several key drivers. The rising popularity of sustainable and eco-friendly construction practices boosts the demand for fair face blocks, which often utilize recycled materials and require less energy in production compared to traditional materials like bricks. Furthermore, the increasing number of commercial and residential construction projects globally, particularly in developing economies experiencing rapid urbanization, significantly contributes to market expansion. The diverse applications of fair face blocks, encompassing both interior and exterior building elements, further broaden market potential. However, the market faces restraints such as fluctuating raw material prices, competition from alternative building materials (like precast concrete and metal cladding), and stringent environmental regulations that impact manufacturing processes. Segmentation reveals strong demand across various block densities (low, medium, and high), with high-density blocks likely leading in commercial applications due to their structural strength. The geographic landscape shows robust growth potential across North America and Asia-Pacific, driven by substantial infrastructure development and ongoing construction activities. Established players like Lignacite, Ibstock, and Aggregate Industries, along with regional manufacturers, are shaping the market's competitive dynamics. The projected market size in 2033, based on the estimated 5% CAGR, would be approximately $1015 million. This projection considers factors like consistent growth in the construction sector, increasing adoption of sustainable building practices, and ongoing innovation in fair face block manufacturing. Market share distribution among regions is expected to remain relatively stable, with North America and Asia-Pacific maintaining significant dominance throughout the forecast period. Continued technological advancements in block manufacturing, focusing on enhanced aesthetics, durability, and energy efficiency, will likely further propel market growth. The successful integration of fair face blocks into modern architectural designs, emphasizing their versatility and design flexibility, will play a critical role in sustained market expansion.

  12. Fairface Emotion

    • kaggle.com
    Updated Jun 11, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Abdul Wasay (2023). Fairface Emotion [Dataset]. https://www.kaggle.com/datasets/abdulwasay551/fairface-emotion/suggestions
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 11, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Abdul Wasay
    Description

    Dataset

    This dataset was created by Abdul Wasay

    Contents

  13. F

    Fair Face Block Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Mar 24, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Archive Market Research (2025). Fair Face Block Report [Dataset]. https://www.archivemarketresearch.com/reports/fair-face-block-78602
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 24, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global fair face block market is a substantial sector, valued at $629 million in 2025 and projected to experience steady growth with a Compound Annual Growth Rate (CAGR) of 3.5% from 2025 to 2033. This growth is driven by several factors. The increasing construction activity in both residential and commercial sectors, particularly in developing economies experiencing rapid urbanization, fuels demand for aesthetically pleasing and durable building materials like fair face blocks. Furthermore, the rising popularity of sustainable and eco-friendly construction practices contributes positively, as fair face blocks often require less energy to produce compared to some alternatives. The market segmentation highlights a diverse range of block types, including low, medium, and high-density blocks, catering to various construction needs and preferences. The application segment further breaks down the market into commercial and residential buildings, reflecting the significant contribution of both sectors to overall market size. Regional variations are also expected, with North America and Europe likely to remain major markets, while Asia-Pacific is poised for significant growth due to expanding infrastructure development and construction projects in rapidly developing nations like China and India. Despite the positive outlook, the market faces certain restraints. Fluctuations in raw material prices, such as cement and aggregates, can impact profitability and pricing. Competition from alternative building materials, such as precast concrete panels and brick, also poses a challenge. However, advancements in block manufacturing techniques, leading to improved aesthetics, strength, and insulation properties, are likely to mitigate these challenges and maintain the market's steady growth trajectory. The diverse range of manufacturers, including both established global players and regional producers, signifies a competitive landscape where innovation and efficiency play crucial roles. The market is expected to see continued expansion over the forecast period, propelled by the ongoing need for durable, aesthetically pleasing, and cost-effective building solutions.

  14. D

    Fair Faced Concrete Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2025). Fair Faced Concrete Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/fair-faced-concrete-market
    Explore at:
    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Fair Faced Concrete Market Outlook



    The global fair-faced concrete market size was valued at approximately USD 8.3 billion in 2023 and is expected to reach around USD 11.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 3.8% during the forecast period. This growth is driven by a combination of aesthetic and functional advantages offered by fair-faced concrete, alongside increasing urbanization and infrastructural developments across the globe.



    One of the primary growth factors for the fair-faced concrete market is its aesthetic appeal. Fair-faced concrete, known for its natural and unadorned look, has become a popular choice among architects and builders who aim to create modern, minimalist designs. The material eliminates the need for additional finishes like plaster or paint, thereby reducing costs and construction time. This factor is particularly advantageous in commercial and residential projects where streamlined construction timelines and budget constraints are critical.



    Additionally, fair-faced concrete offers excellent durability and low maintenance, which makes it an attractive option for infrastructure projects such as bridges, tunnels, and public buildings. Its resilience against weathering and aging ensures a long lifespan, reducing the need for frequent repairs or replacements. This attribute is particularly important in regions susceptible to extreme weather conditions, further propelling market growth. Environmental sustainability is another compelling factor driving the fair-faced concrete market. As an eco-friendly building material, it involves fewer chemical treatments and coatings, resulting in lower environmental impact. Moreover, the rising awareness of green building practices and the increasing adoption of LEED (Leadership in Energy and Environmental Design) certified projects are expected to bolster the demand for fair-faced concrete over the forecast period.



    The regional outlook for the fair-faced concrete market indicates significant growth potential in the Asia Pacific region, primarily driven by rapid urbanization and infrastructural developments in countries such as China and India. The North American market is also anticipated to grow steadily, supported by advancements in construction technology and an increasing focus on sustainable building practices. European markets, influenced by stringent environmental regulations and a high inclination towards aesthetically pleasing construction materials, are expected to exhibit moderate growth. Meanwhile, the Middle East & Africa and Latin American regions are likely to experience gradual expansion, driven by infrastructural investments and urban development projects.



    In the realm of concrete production, Dry-Cast Concrete stands out as a significant method, particularly for projects requiring high precision and strength. Unlike traditional wet-cast methods, dry-cast concrete involves a low water-to-cement ratio, resulting in a zero-slump mix that is compacted using vibration. This technique is highly efficient for producing concrete products such as pipes, blocks, and pavers, where uniformity and durability are paramount. The dry-cast process not only accelerates production times but also enhances the structural integrity of the final product, making it a preferred choice for infrastructure projects. As the demand for robust and sustainable construction materials grows, the role of dry-cast concrete in the industry is likely to expand, offering a reliable solution for modern construction challenges.



    Type Analysis



    The fair-faced concrete market is segmented into two primary types: precast and cast-in-place. Precast fair-faced concrete is manufactured off-site in controlled environments and then transported to the construction site for installation. This method offers several benefits, including reduced construction time and improved quality control, making it a popular choice for large-scale commercial and infrastructure projects. The use of precast concrete also minimizes labor costs and site disruptions, which are crucial factors in densely populated urban areas. Furthermore, advancements in precast technology, such as the development of high-performance concrete and innovative formwork systems, have enhanced the aesthetic and structural capabilities of precast fair-faced concrete, contributing to its growing adoption.



    On the other hand, cast-in-place fair-faced concrete is poured and cured on-site, allowing for greater flexibility in design an

  15. F

    Fair Face Block Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Jul 23, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Market Report Analytics (2025). Fair Face Block Report [Dataset]. https://www.marketreportanalytics.com/reports/fair-face-block-156370
    Explore at:
    pdf, ppt, docAvailable download formats
    Dataset updated
    Jul 23, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global fair face block market, valued at $498 million in 2025, is projected to experience steady growth, driven by the increasing demand for aesthetically pleasing and durable building materials in both residential and commercial construction. The 3.5% CAGR suggests a consistent expansion over the forecast period (2025-2033), fueled by several key factors. Rising urbanization and infrastructure development projects worldwide are significantly boosting demand. Furthermore, the architectural preference for exposed brick and concrete aesthetics in modern design contributes to the market's growth trajectory. The market also benefits from the inherent advantages of fair face blocks, such as their strength, durability, and ease of installation, making them a cost-effective and efficient construction solution. However, potential restraints include fluctuations in raw material prices (cement, aggregates) and increasing competition from alternative cladding materials like metal panels and composite materials. The market is segmented geographically, with North America and Europe likely holding significant market shares due to their mature construction sectors and established building codes favoring such materials. Leading players like Lignacite, O'Reilly Barleystone, and Ibstock are leveraging technological advancements and strategic partnerships to enhance their market position and product offerings. The competitive landscape features both established industry giants and smaller regional players. Success in this market hinges on factors such as product innovation (introducing new textures, colors, and sizes), efficient production processes to optimize costs, and a robust distribution network. Future growth will depend on adapting to evolving architectural trends, sustainability concerns (incorporating eco-friendly materials), and technological improvements in manufacturing and installation techniques. The market's sustained growth outlook is underpinned by the enduring need for durable, visually appealing, and cost-effective building solutions. Innovation in design, material science, and production processes will play a crucial role in shaping the future of this market segment.

  16. h

    The Laval Face+Lighting HDR Dataset

    • hdrdb.com
    exr
    Updated Oct 15, 2019
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Université Laval (2019). The Laval Face+Lighting HDR Dataset [Dataset]. http://doi.org/10.1145/3130800.3130891
    Explore at:
    exrAvailable download formats
    Dataset updated
    Oct 15, 2019
    Dataset provided by
    Université Laval
    Description

    9 subjects were recruited for this task and were asked to be photographed with a mostly neutral expression. They were photographed under 25 different lighting conditions. There were 8 male and 1 female subjects, most with fair skin. Subjects had varying amounts of facial hair, ranging from none to full beards. Each shooting session was performed in the following sequence of steps. First, another Canon 5D Mark III camera mounted on a robotic tripod at the planned facial capture location, and an exposure bracketed sequence of photographs was captured at different orientations, and merged into an HDR spherical environment map of the illumination conditions. Second, the tripod was removed and each subject was asked to stand at the same location, one at a time, in quick succession. In all, we performed 25 such shooting sessions for a total of 137 face/lighting pairs.

  17. h

    Continuous-Ethnicity-Face-Recognition

    • huggingface.co
    Updated Jun 10, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pedro C. Neto (2025). Continuous-Ethnicity-Face-Recognition [Dataset]. https://huggingface.co/datasets/netopedro/Continuous-Ethnicity-Face-Recognition
    Explore at:
    Dataset updated
    Jun 10, 2025
    Authors
    Pedro C. Neto
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset Card for Ethnicity Fairness in a Continuous Space

      Dataset Details
    
    
    
    
    
      Dataset Description
    

    This dataset provides the training images (from BalancedFace and GlobalFace produced by BUPT) used in the paper: "Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognition". These have been curated to be balanced in a continuous ethnicity space, following three different strategies: Protocol A, Protocol B and Protocol C. In… See the full description on the dataset page: https://huggingface.co/datasets/netopedro/Continuous-Ethnicity-Face-Recognition.

  18. h

    FairFace_Balanced_3K

    • huggingface.co
    Updated Aug 6, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Subhansh Malviya (2025). FairFace_Balanced_3K [Dataset]. https://huggingface.co/datasets/Subh775/FairFace_Balanced_3K
    Explore at:
    Dataset updated
    Aug 6, 2025
    Authors
    Subhansh Malviya
    License

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

    Description

    FairFace_Balanced_3K

      Overview
    

    FairFace_Balanced_3K is a balanced subset of the original HuggingFaceM4/FairFace dataset created to support bias-sensitive experiments in facial attribute recognition. This subset includes 3,031 samples, with 433 images per race class, across 7 race categories:

    White
    Black
    East Asian
    Southeast Asian
    Indian
    Middle Eastern
    Latino_Hispanic

    Each entry contains:

    RGB facial image Age group label (9 categories) Gender label (Male, Female) Race… See the full description on the dataset page: https://huggingface.co/datasets/Subh775/FairFace_Balanced_3K.

  19. f

    Clothing colour choices to match fair and tanned skin types of White women

    • figshare.com
    txt
    Updated May 14, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    David Perrett (2021). Clothing colour choices to match fair and tanned skin types of White women [Dataset]. http://doi.org/10.6084/m9.figshare.14596905.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 14, 2021
    Dataset provided by
    figshare
    Authors
    David Perrett
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Excel xlsx and csv data sheets for 2 experiments. Each row represents one trial with one participant and defines the colour of simulated clothing (in Hue, Saturation and Value; Red, Green and Blue; and CIE L*a*b* colour spaces) the participant chose to match a facial image of a White woman with fair or relatively tanned skin. Participants chose clothing colour from two colour ranges: (1) defined by hue 0-360 degrees and Value 0-1.0 with Saturation fixed at 1.0 (2) defined by hue 0-360 degrees and Saturation 0-1, with Value fixed at .0)experiment 1: 96 participants, 12 facesexperiment 2: 95 participants, 8 facesThe skin colour of the 12 stimulus faces (in CIE L*a*b* colour space) is specified on a separate sheet and csv file.

  20. F

    Fair Faced Concrete Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated May 23, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Archive Market Research (2025). Fair Faced Concrete Report [Dataset]. https://www.archivemarketresearch.com/reports/fair-faced-concrete-372158
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    May 23, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global fair-faced concrete market is experiencing robust growth, driven by increasing infrastructure development, rising urbanization, and a growing preference for aesthetically pleasing and durable construction materials. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated value of $28 billion by 2033. This growth is fueled by several key trends, including the adoption of sustainable construction practices, advancements in concrete technology leading to improved durability and aesthetic appeal, and the increasing demand for precast concrete elements in building construction. Major players like BASF, Sika, and Holcim are significantly contributing to market expansion through continuous innovation and strategic partnerships. However, the market faces challenges such as fluctuating raw material prices and potential labor shortages in certain regions. Despite these restraints, the long-term outlook remains positive, driven by the ongoing global construction boom and the growing awareness of fair-faced concrete's inherent benefits. The segmentation of the fair-faced concrete market is primarily based on application (residential, commercial, industrial, infrastructure), type (precast, cast-in-situ), and region. The infrastructure segment is expected to dominate due to large-scale projects globally. While detailed regional data is unavailable, it's reasonable to assume a significant market share distribution across North America, Europe, and Asia-Pacific, reflecting the high levels of construction activity in these regions. Competition within the market is intense, with numerous multinational companies and regional players vying for market share. These companies focus on providing innovative solutions, enhancing product quality, and expanding their geographical reach to maintain competitiveness. The market's future trajectory hinges on the sustained growth of the construction industry, the implementation of sustainable building practices, and the ongoing evolution of fair-faced concrete technology.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
HuggingFaceM4 (2023). FairFace [Dataset]. https://huggingface.co/datasets/HuggingFaceM4/FairFace

FairFace

HuggingFaceM4/FairFace

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 2, 2023
Dataset authored and provided by
HuggingFaceM4
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Dataset Card for FairFace

  Dataset Summary

FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/FairFace.
Search
Clear search
Close search
Google apps
Main menu