task_categories:image-classification language:en license:cc-by-nc-nd-4.0 code finance region:us
The dataset is primarly intended to dentify and predict the positions of major
joints of a human body in an image. It consists of people's photographs with
body part labeled with keypoints.
@InProceedings{huggingface:dataset,
title = {pose_estimation},
author = {TrainingDataPro},
year = {2023}
}
---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
language:
- en
tags:
- code
- finance
dataset_info:
  features:
  - name: image_id
    dtype: uint32
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    dtype: string
  splits:
  - name: train
    num_bytes: 142645152
    num_examples: 29
  download_size: 137240523
  dataset_size: 142645152
---
# Pose Estimation
The dataset is primarly intended to dentify and predict the positions of major joints of a human body in an image. It consists of people's photographs with body part labeled with keypoints.

# Get the dataset

### This is just an example of the data

Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) to discuss your requirements, learn about the price and buy the dataset.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F31b38dee8dc63c581004afcf82136116%2F12.jpg?generation=1684357817470094&alt=media)

# Data Format

Each image from `EP` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the key points. For each point, the x and y coordinates are provided, and there is a `Presumed_Location` attribute, indicating whether the point is presumed or accurately defined.

# Example of XML file structure
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc8b7cc938539368c9ec03dd01a26724c%2Fcarbon%20(1).png?generation=1684358333663868&alt=media)

# Labeled body parts
Each keypoint is ordered and corresponds to the concrete part of the body:
0. **Nose**
1. **Neck**
2. **Right shoulder**
3. **Right elbow**
4. **Right wrist**
5. **Left shoulder**
6. **Left elbow**
7. **Left wrist**
8. **Right hip**
9. **Right knee**
10. **Right foot**
11. **Left hip**
12. **Left knee**
13. **Left foot**
14. **Right eye**
15. **Left eye**
16. **Right ear**
17. **Left ear**


# Keypoint annotation is made in accordance with your requirements.

## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**

TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**