Hair Detection & Segmentation Dataset

Dataset Features

• Diverse Collection: This dataset includes pictures of people with many different hair styles, colors, lengths, and textures. Therefore, this diversity makes the models trained on this data more robust and versatile.

• High-Quality Annotations: Every image comes with detailed notes, clearly marking the edges and shapes of individual hair strands within the facial oval. These notes are essential for training accurate hair segmentation models.

• Varied Lighting Conditions: Images are captured under different lighting conditions to make the dataset more applicable to real-world scenarios. This way, models trained on this dataset will perform well in various environments. Additionally, the varied lighting ensures robustness, which is crucial for real-world applications.

• Multiple Angles and Perspectives: The dataset includes images taken from various angles and perspectives, providing a comprehensive view of the hair and improving the generalizability of the segmentation models.
• Demographic Diversity: The dataset features individuals from various age groups, ethnic backgrounds, and genders, promoting inclusivity and reducing bias in the developed models.

Applications

• Virtual Makeovers: Enhance virtual makeover applications by accurately detecting and segmenting hair, allowing for realistic hair style simulations and color changes.
• Augmented Reality: Improve AR applications that involve hair modifications, such as virtual try-ons for hair products or accessories.
• Medical and Research: Assist in medical research and applications related to dermatology and trichology by providing accurate hair segmentation.
• Film and Animation: Aid in the development of realistic hair effects in film, animation, and video game industries.