Use Case



Explore EasyPortrait, a 26GB dataset with 20,000 high-quality RGB images and annotated masks for advanced portrait segmentation and face parsing.


About the Dataset

Introducing EasyPortrait, a comprehensive dataset created for advanced portrait segmentation and face parsing. This large-scale image dataset is thoughtfully assembled to support various applications, such as background removal in video conferencing, teeth whitening, face skin enhancement, red-eye removal, and eye colorization. Moreover, what sets EasyPortrait apart is its high-quality annotations and diverse range of images. Therefore, it serves as an invaluable resource for researchers and developers in the field of computer vision.

Dataset Overview

The EasyPortrait dataset is extensive, encompassing approximately 26GB of data and featuring 20,000 RGB images. Notably, around 17,500 of these images are in Full HD resolution, ensuring exceptional detail and clarity. Each image comes with high-quality annotated masks, which help in precise segmentation and parsing tasks. The dataset is methodically divided into training, validation, and test sets based on the subject’s user ID.

  • Training Set: 14,000 images
  • Validation Set: 2,000 images
  • Test Set: 4,000 images


The EasyPortrait dataset is versatile and can be employed in numerous tasks, including but not limited to:

  • Background Removal: Seamlessly remove backgrounds in video conferencing applications, enhancing the user’s visual experience.
  • Teeth Whitening: Apply algorithms to whiten teeth in portraits, contributing to aesthetic improvements in images.
  • Face Skin Enhancement: Enhance skin texture and tone in facial images, promoting better cosmetic outcomes.
  • Red Eye Removal: Correct red-eye effects in photographs, resulting in more natural-looking images.
  • Eye Colorization: Change or enhance eye color in images, providing creative and realistic modifications.

Dataset Structure

To ensure optimal usability and performance, the EasyPortrait dataset is organized as follows:

  1. Training Set: Contains 14,000 images, providing a substantial amount of data for model training.
  2. Validation Set: Comprises 2,000 images, enabling the fine-tuning of models and hyperparameter adjustments.
  3. Test Set: Includes 4,000 images, reserved for evaluating model performance and ensuring robust generalization.

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