---
title: "Men Hair Loss Segmentation Dataset"
description: "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…"
url: "https://unidata.pro/datasets/hair-loss-in-men-segmentation-dataset/"
date_modified: "2026-03-23T14:11:50+03:00"
language: "en-US"
---
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.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 3 100 — **Text:** Images
- **Number:** 775 — **Text:** People

### Tooltips Section

**Tooltip items:**

- **Name:** Medicine
- **Name:** Classification
- **Name:** Computer Vision
- **Name:** Machine Learning
- **Name:** Segmentation

### Dataset Information

**Table with data:**

| 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 |

**Media Slider:**

- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/men-hair-loss-segmentation.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/men-hair-loss-segmentation2.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/men-hair-loss-segmentation4.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/men-hair-loss-segmentation3.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1Fwgao0u-1c02P9ZHsPL-skFyI126yM5Y?usp=sharing)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image Extensions | Png, jpeg |
| Mask Extensions | Png |

**Source and data collection methodology:** Source and collection methodology: Data was collected via crowdsourcing platforms.

### LLM Languages

**Section Title:** Statistics

**List of Statistics:**

- **Filter by:** Top 10 Country Distribution — **GIF image:** Top 10 Countries by Distribution — **Table with data:**

| Country | COUNTA of country |
| --- | --- |
| RU | 442 |
| TR | 36 |
| PK | 35 |
| BR | 34 |
| KZ | 26 |
| IN | 26 |
| BY | 23 |
| PH | 20 |
| UA | 18 |
| US | 13 |

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id="75"] — **caption above the graph:** Ethnicity Distribution
- **Shortcode:** [ays_chart id="74"] — **caption above the graph:** Continent Distribution

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Medical Research & Dermatology — **Title:** Early Diagnosis of Alopecia in Men — **Text:** 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.
- **Industry:** Pharmaceutical & Hair Restoration — **Title:** Training Models for Hair Care and Treatment Solutions — **Text:** 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.
- **Industry:** Artificial Intelligence & Computer Vision — **Title:** Developing Segmentation Models for Scalp Analysis — **Text:** 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.
- **Industry:** Consumer Applications & Wellness Tech — **Title:** Supporting Hair Care Apps and Digital Consultations — **Text:** 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.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What should I consider before buying this dataset? — **Answer:** 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.
- **Question:** How was the data collected? — **Answer:** 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.
- **Question:** What types of annotations are provided? — **Answer:** 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.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets are provided via a dual licensing model: free trial samples are included, while full datasets require payment.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. Our datasets adhere strictly to GDPR and relevant data protection requirements. Data collection relies on lawful and permissible sources to ensure proper usage.
- **Question:** How are Unidata datasets stored? — **Answer:** Unidata uses AWS cloud infrastructure to store datasets, ensuring resilience, scalability, and strong security. We maintain compliance with ISO 27001 and ISO 27701, guaranteeing internationally accepted data protection standards. This approach ensures a robust, privacy-aware, and secure environment for data handling.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** Yes, this is a real-world dataset. Our Men Hair Loss Segmentation Dataset contains photos of men with varying degrees of baldness and alopecia, captured from real individuals to support machine learning models in hair loss segmentation and analysis of scalp conditions.
- **Question:** How do segmentation masks improve hair loss detection models? — **Answer:** Unlike classification datasets, this dataset provides pixel-level segmentation masks that precisely identify hair and scalp regions. These annotations enable deep learning models to measure hair density, estimate bald areas, and perform accurate scalp segmentation.

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