Semantic face segmentation is a crucial aspect of computer vision, where each pixel of an image containing a face is assigned a semantic label, such as "skin," "eye," or "nose".
We provide extensive annotated datasets of images with corresponding per-pixel label maps. These maps have been meticulously created by human annotators, ensuring high-quality, consistent, and complete annotations.
Our semantic-face datasets are characterized by four key features:
- Diversity: Our datasets include a wide range of faces, encompassing various ethnicities, ages, genders, lighting conditions, and poses.
- Quality annotations: We prioritize accuracy and consistency in our annotations, to guarantee the best performance of the models.
- Large size: Our datasets are extensive, leading to more robust models.
- Representativeness: Our datasets accurately reflect the distribution of faces that the model will encounter in real-world scenarios, which we achieve through the use of crowd-solving platforms.
We are equipped to efficiently gather and annotate the required dataset for your specific task.