High-Resolution Clothing Segmentation Dataset for Deep Learning

The images were collected and organized to train, validate, and test segmentation models that can identify and isolate individual garments from the background. The data are structured into images and binary masks, but the masks can also be converted into annotation formats such as .txt or .json if required for specific frameworks.

The dataset is organized into three main folders: Train: Images: 3,067 RGB images of clothing items (.jpg) Masks: 3,067 corresponding binary segmentation masks (.png) Valid: Images: 564 RGB images (.jpg) Masks: 564 binary masks (.png) Laboratory: Images: 347 RGB images captured in a laboratory environment (.jpg) Masks: 347 binary masks (.png) The clothing items are typically laid flat (not worn by people) and photographed with high resolution camera.