Commercial

Men Hair Loss Segmentation Dataset

Men Hair Loss Segmentation Dataset is an accurately labeled bald men dataset consisting of multi-angle images of individuals with varying degrees of hair loss, labeled for alopecia classification, and designed to support machine learning models in analyzing scalp conditions, hair thinning, and developing hair restoration and hair transplantation solutions using advanced deep learning techniques.

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  • Images
    3 100
  • People
    775
  • Medicine
  • Classification
  • Computer Vision
  • Machine Learning
  • Segmentation
  • Images
    3 100
  • People
    775

Dataset Info

Characteristic Data
Description Photos of men with varying degrees of hair loss for segmentation tasks
Data types Image
Tasks Classification, Machine Learning
Number of images 3 100
Number of files in a set 4 images per person (image from the top + mask, image from front + mask)
Total number of people 775
Labeling Metadata (gender, age, ethnicity)
Age Min = 18, max = 80, mean = 45
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Technical
Characteristics

Characteristic Data
Image Extensions Png, jpeg
Mask Extensions Png
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Medical Research & Dermatology

    Early Diagnosis of Alopecia in Men

    Men Hair Loss Segmentation Dataset contains labeled images of bald men that capture different stages of alopecia. Dermatology researchers use this hair loss segmentation dataset to study scalp conditions, analyze hair follicles, and improve methods for early detection of hair disorders. It helps doctors assess hair thinning and supports clinical research into hair loss treatments.



  • Pharmaceutical & Hair Restoration

    Training Models for Hair Care and Treatment Solutions

    This alopecia dataset enables pharmaceutical companies to design hair restoration and hair transplantation solutions with higher precision. By using training datasets with segmentation masks, developers can evaluate how learning models detect hair density variations, predict treatment outcomes, and support innovations in hair growth and regenerating hair therapies tailored to different skin types.



  • Artificial Intelligence & Computer Vision

    Developing Segmentation Models for Scalp Analysis

    AI engineers leverage this bald detection dataset to build deep learning algorithms for hair segmentation and scalp health monitoring. The dataset includes medical images with pixel-level annotations, allowing neural networks to achieve the highest precision in identifying hair textures, alopecia areata, and other skin diseases, which significantly improves automated diagnosis and machine learning outcomes.



  • Consumer Applications & Wellness Tech

    Supporting Hair Care Apps and Digital Consultations

    Wellness platforms and mobile apps utilize the bald people dataset to power object detection tools that monitor hair loss, assess scalp health, and recommend personalized hair care routines. With trained models, apps can offer accurate diagnosis and recommend loss treatments, helping users achieve healthier hairs and reduce the risk of hair thinning.



FAQs

What is included in Men Hair Loss Segmentation Dataset?
The dataset includes 3,100 images of 775 men with varying degrees of hair loss. Each person has 4 files, including front and top views with segmentation masks, along with metadata such as age, gender, and ethnicity.
What should I consider before buying this dataset?
When purchasing the dataset, consider whether the image quality, segmentation masks, and metadata fit your project. Check the dataset’s diversity in age groups, ethnicities, and hair loss types to ensure it supports accurate machine learning and deep learning tasks.
How was the data collected?
This dataset was created through structured image captures of men aged 18 to 80 across multiple ethnic groups. The data was carefully prepared with manual annotations to reflect different stages of hair thinning and alopecia areata for deep learning models.
What types of annotations are provided?
The dataset includes binary segmentation masks for detecting hair follicles and scalp regions. In addition, it provides metadata annotations like age, gender, and ethnicity, enabling trained models to achieve the highest precision in hair loss detection and treatment planning.
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