---
title: "Body Measurements Image Dataset"
description: "The dataset contains images of the human body with corresponding text files and detailed anthropometric measurements, including body scans, body shapes, and circumference annotations, making…"
url: "https://unidata.pro/datasets/body-measurements-image-dataset/"
date_modified: "2026-03-23T13:36:50+03:00"
language: "en-US"
---
The dataset contains images of the human body with corresponding text files and detailed anthropometric measurements, including body scans, body shapes, and circumference annotations, making it a valuable body measurements dataset that provides precise training data for AI models, personalized recommendations in e-commerce, pose estimation, and computer vision applications

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 943 — **Text:** Images
- **Number:** 43 — **Text:** People

### Tooltips Section

**Tooltip items:**

- **Name:** Segmentation
- **Name:** Computer Vision
- **Name:** Machine Learning
- **Name:** Object Detection
- **Name:** Pose Estimation

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Image of people for body segmentation |
| Data types | Image |
| Tasks | Human Segmentation, Pose Estimation, Computer Vision |
| Number of people | 43 |
| Number of files in a set | 13 images (side, front, pelvis circumference, chest circumference, hips circumference, front build, calf circumference, upper arm, under chest, arm length, thigh circumference, arm circumference, waist circumference) |
| Total number of files | 943 |
| Labeling | Metadata (ID, height, weight, age, gender, race, profession) and annotation (arm circumference(cm), arm length(cm), calf circumference(cm), chest circumference(cm), front build(cm),	hips circumference(cm), pelvis circumference(cm), thigh circumference(cm), under chest circumference(cm), upper arm length(cm), waist circumference(cm)) |
| Gender | Male, Female |
| Ethnicity | Caucasian, Indian, African, Latino Hispanic, Maghreb |

**Media Slider:**

- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/arm_length.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/arm_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/calf_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/chest_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/front_build.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/front_img.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/hips_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/pelvis_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/side_img.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/thigh_circumference.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/under_chest.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/upper_arm.webp)
- **Image in the slider:** ![body measurement dataset](https://unidata.pro/wp-content/uploads/2025/06/waist_circumference.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1xYu1dEZ7hgkoMw0X99l_KU9Jrj5oRVYA?usp=sharing)

### LLM Languages

**Section Title:** Statistics

**List of Statistics:**

- **Filter by:** Age Distribution — **GIF image:** Age Distribution — **Table with data:**

| Age | Count |
| --- | --- |
| Under 18 | 10 |
| 19-25 | 10 |
| 26-32 | 9 |
| 33-39 | 9 |
| 40-46 | 4 |
| 47-53 | 0 |
| 54-59 | 1 |
| 60-66 | 0 |
| 67+ | 0 |
- **Filter by:** Height Distribution — **GIF image:** Height Distribution — **Table with data:**

| Height | COUNTA of height |
| --- | --- |
| 190 | 1 |
| 186 | 2 |
| 185 | 1 |
| 184 | 1 |
| 182 | 1 |
| 181 | 1 |
| 177 | 1 |
| 176 | 2 |
| 175 | 3 |
| 173 | 1 |
| 172 | 2 |
| 170 | 3 |
| 168 | 2 |
| 167 | 1 |
| 165 | 4 |
| 163 | 2 |
| 162 | 2 |
| 161 | 2 |
| 160 | 2 |
| 159 | 2 |
| 158 | 1 |
| 157 | 1 |
| 156 | 1 |
| 155 | 3 |
| 152 | 1 |
| Grand Total | 43 |
- **Filter by:** Weight Distribution — **GIF image:** Weight Distribution — **Table with data:**

| Weight | COUNTA of weight |
| --- | --- |
| 42 | 1 |
| 43 | 1 |
| 44 | 1 |
| 49 | 1 |
| 50 | 1 |
| 52 | 1 |
| 53 | 2 |
| 54 | 2 |
| 55 | 3 |
| 56 | 1 |
| 58 | 1 |
| 59 | 1 |
| 60 | 2 |
| 61 | 1 |
| 62 | 1 |
| 63 | 1 |
| 64 | 1 |
| 65 | 1 |
| 70 | 3 |
| 74 | 1 |
| 75 | 2 |
| 76 | 1 |
| 77 | 1 |
| 78 | 2 |
| 80 | 2 |
| 82 | 1 |
| 83 | 1 |
| 89 | 1 |
| 93 | 1 |
| 99 | 1 |
| 100 | 1 |
| 104 | 1 |
| 112 | 1 |
| Grand total | 43 |

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id='62'] — **caption above the graph:** Gender Distribution
- **Shortcode:** [ays_chart id='63'] — **caption above the graph:** Ethnicity Distribution
- **Shortcode:** [ays_chart id='64'] — **caption above the graph:** Profession Distribution

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image extension | JPG |
| Annotation extension | JSON |

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

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Fashion & E-Commerce — **Title:** Clothing Fit Prediction Models — **Text:** Body Measurements Image Dataset helps fashion retailers develop systems that improve clothing size recommendations. Since the dataset consists of people in swimsuits with detailed annotations, it supports body segmentation and pose estimation. By using this clothing dataset, companies can train vision models to predict body shapes and achieve more accurate virtual try-on experiences.
- **Industry:** Healthcare & Fitness — **Title:** Body Shape Analysis and Monitoring — **Text:** Healthcare providers and fitness platforms rely on this human segmentation dataset for analyzing body parts and tracking human body changes over time. The dataset offers manually annotated images with segmentation masks, allowing deep learning systems to measure proportions and progress. This improves applications in weight management, rehabilitation, and personalized training methods based on segmentation accuracy.
- **Industry:** Computer Vision Research — **Title:** Advancing Human Segmentation Tasks — **Text:** Research labs use the swimsuit human segmentation dataset as a benchmark for semantic segmentation and instance segmentation tasks. The training set contains RGB images, manually segmented with binary segmentation masks and corresponding annotations. These properties make it one of the strongest benchmark datasets, helping deep neural networks achieve high accuracy in automatic segmentation and image classification.
- **Industry:** AI & Technology Development — **Title:** Improving Deep Learning Models — **Text:** The images in the swimsuit dataset are essential for developing computer vision tools in human parsing and body-part segmentation. As the dataset includes original images with manual annotations, it reduces the need for manually labeling large-scale data collections. By integrating this body part segmentation dataset into pipelines, companies significantly improve recognition systems and enhance future vision tasks.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What types of annotations are provided? — **Answer:** The dataset includes rich anthropometric data, covering measurements like pelvis circumference, hip circumference, thigh circumference, upper arm length, and more. These annotations enable the development of 3D models, body scans, and precise body shape recognition systems for research and commercial use.
- **Question:** How was the data collected? — **Answer:** This dataset was created using structured image captures of 43 participants across diverse demographics, including Caucasian, Indian, African, Latino Hispanic, and Maghreb groups. The process ensures a wide range of human body shapes and anthropometric measurements, making it highly reliable as training data and a test set for AI and data analysis.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes, a sample can usually be requested to help evaluate the dataset. Reviewing a sample lets you check the image quality, annotation format (JSON), and body measurement metadata to confirm that it fits your computer vision or AI training needs.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. We ensure datasets are compliant with GDPR and data protection laws. Data is sourced only from legitimate, legally approved channels.
- **Question:** How are Unidata datasets stored? — **Answer:** Datasets managed by Unidata are securely stored within AWS’s cloud environment, ensuring seamless scalability and uptime. Our compliance with ISO 27001 and ISO 27701 guarantees adherence to global security and privacy standards. This approach ensures robust protection and reliable management of all data.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets comply with a dual licensing model: free samples are accessible for testing, while full datasets require purchase.
- **Question:** Is it unique data? — **Answer:** Yes, the dataset is unique. We collect it independently using our own processes, and it is not available from any open sources. The data is created exclusively for our clients and stored securely, with access provided only upon purchase.
- **Question:** Why is this dataset suitable for virtual try-on systems? — **Answer:** The dataset contains diverse human body types and measurement information, enabling virtual fitting algorithms to estimate clothing sizes more accurately. It supports AI solutions for fashion technology, e-commerce, and personalized apparel recommendations.

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)