AdaBins is an official implementation of a depth estimation method that uses a U-Net architecture with adaptive binning to predict metric depth from monocular images. The model divides the depth range into learnable bins whose centers adapt based on the input image content, and regresses bin edges alongside depth values. Pretrained weights are provided for NYU-Depth-v2 and KITTI datasets.