task_categories:image-segmentation task_categories:image-classification language:en license:cc-by-nc-nd-4.0 code region:us
The dataset consists of images of parking spaces along with corresponding bounding box
masks. In order to facilitate object detection and localization, every parking space in
the images is annotated with a bounding box mask.
The bounding box mask outlines the boundary of the parking space, marking its position
and shape within the image. This allows for accurate identification and extraction of
individual parking spaces. Each parking spot is also labeled in accordance to its
occupancy: free, not free or partially free.
This dataset can be leveraged for a range of applications such as parking lot
management, autonomous vehicle navigation, smart city implementations, and traffic
analysis.
@InProceedings{huggingface:dataset,
title = {hair-detection-and-segmentation},
author = {TrainingDataPro},
year = {2023}
}
---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-segmentation
- image-classification
tags:
- code
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: collage
    dtype: image
  - name: shapes
    dtype: string
  splits:
  - name: train
    num_bytes: 482079410
    num_examples: 98
  download_size: 478206925
  dataset_size: 482079410
---

# Hair Detection & Segmentation Dataset

The dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.

The dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*.  Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.

The dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F81b5a9e6c755e04d97fc6b175a127432%2FMacBook%20Air%20-%201.png?generation=1691561622573906&alt=media)

# 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=hair-detection-and-segmentation) to discuss your requirements, learn about the price and buy the dataset.

# Dataset structure
- **images** - contains of original images of people
- **masks** - includes segmentation masks for the original images
- **collages** - includes original images with colored hairs within the oval of the face
-  **annotations.xml** -  contains coordinates of the bounding boxes and labels, created for the original photo

# Data Format

Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.

### Tags for the images:
- **is_hair** - contains of original images of people
- **no_hair** - includes segmentation masks for the original images

# Example of XML file structure
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb634cd569d4bf7a253ac7a0e7a91ef7e%2Fcarbon.png?generation=1691562068420789&alt=media)

# Hair Detection & Segmentation might be made in accordance with your requirements.

## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=hair-detection-and-segmentation) 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**