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Male Hair Loss Dataset

The hair loss dataset contains high-resolution scalp images of people captured from five sides, labeled with seven classes on the Norwood-Hamilton scale and supplemented with hair follicle annotations to help machine learning models analyze hair density, thinning patterns, and diagnose baldness

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  • Images
    2 400
  • People
    480
  • Computer Vision
  • Medicine
  • Classification
  • Machine Learning
  • Data Labeling

The hair loss dataset contains high-resolution scalp images of people captured from five sides, labeled with seven classes on the Norwood-Hamilton scale and supplemented with hair follicle annotations to help machine learning models analyze hair density, thinning patterns, and diagnose baldness

Get in touch Download sample
  • Computer Vision
  • Medicine
  • Classification
  • Machine Learning
  • Data Labeling
  • Images
    2 400
  • People
    480

Dataset Info

Characteristic Data
Description Photos of people with varying degrees of hair loss for alopecia classification
Data types Image
Tasks Classification, Machine Learning
Number of images 2 400
Number of files in a set 5 images (full-face photo, view from the top, back of the head, left side and right side)
Total number of people 480
Labeling Metadata (gender, age, ethnicity)
Age Min = 18, max = 80, mean = 45
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Technical
Characteristics

Characteristic Data
Image Extensions Png, jpeg
Extension of labeling file txt
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Healthcare & Dermatology

    Supporting Early Diagnosis of Hair Disorders

    Male Hair Loss Dataset provides high-quality medical images of different stages of baldness, mapped to the Norwood scale dataset. Dermatologists and researchers can use it to study scalp conditions, hair thinning, and alopecia areata, supporting early detection and improving treatment planning for patients with hair disorders.



  • AI & Machine Learning

    Training Models for Baldness Detection

    This hair loss dataset is widely used to develop machine learning and deep learning models for bald detection. With labeled bald images datasets, developers can build neural networks that classify hair density, detect hair falling patterns, and provide reliable insights for hair restoration and scalp health research.



  • Cosmetic & Hair Care Industry

    Improving Hair Restoration Solutions

    The bald men dataset helps cosmetic companies and clinics refine hair transplants and hair care solutions. By analyzing hair textures, skin types, and hair follicles, businesses can design more personalized hair loss treatments that promote thicker hair, better scalp health, and improved outcomes in hair transplantation procedures.



  • Research & Development

    Building Accurate Diagnostic Tools

    This alopecia dataset serves as reliable training data for researchers exploring new anti-hair loss techniques. By combining dermoscopic images and varied human skin types, it enables the creation of trained models that achieve higher precision in diagnosing hair disorders, supporting advances in artificial intelligence for dermatology and hair growth research.



FAQs

What is Male Hair Loss Dataset used for?
The dataset is used for classification tasks, machine learning, and deep learning models in alopecia research, baldness detection, and scalp health studies. It supports neural networks and learning algorithms for early diagnosis, hair restoration treatments, and accurate detection of hair thinning, hair follicle issues, and scalp conditions.
What types of images are included?
It contains 2,400 images of 480 individuals with different stages of hair thinning and baldness. Each subject has five views (front, top, back, left, right), along with metadata such as gender, age, and ethnicity, making it suitable for hair restoration, scalp condition research, and AI-based alopecia detection.
Can I request a sample of the Male Hair Loss Dataset before I make a purchase?
Yes, Unidata provides samples for evaluation. You can review image quality, annotation formats, and scalp condition diversity to confirm the dataset’s suitability for machine learning, early detection of alopecia areata, hair density analysis, and hair restoration research projects.
What should I consider before buying this dataset?
Before purchasing, consider your training objectives, whether for Norwood scale analysis, scalp condition diagnosis, or alopecia classification. Evaluate the image diversity, labeling quality, and metadata details, since these factors impact the performance of deep learning models, early diagnosis systems, and hair restoration treatment research.
How is the data stored?
Each Unidata dataset is hosted on AWS cloud systems, ensuring high-performance storage and scalability. Security and privacy practices are aligned with ISO 27001 and ISO 27701 frameworks, delivering compliance with recognized international requirements. This provides strong assurance of safe and responsible data management.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. Every dataset is GDPR-compliant and adheres to applicable data protection laws. Data is sourced exclusively from permissible and legal channels.
How long does it take to receive the dataset?
Once you submit your request, we will contact you to confirm the details and finalize the required documents. After signing and completing the payment, the dataset will be delivered within 3–10 days.
Is this a real-world dataset or synthetic data?
This is a real-world dataset. The dataset contains photos of people with varying degrees of hair loss for alopecia classification, collected via crowdsourcing platforms. All images are authentic, depicting real individuals across different ages, genders, and ethnicities, suitable for machine learning and classification tasks.
Still have questions about using Unidata datasets? Read our user-guides

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