Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al., and was first released in this repository.

Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone.

The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation.

Evaluated on 512 landscape images from the COCO dataset with aspect ratio similar to 4:3. Images were streched to a fixed size of 518x396, and the groundtruth corresponds to the results from the PyTorch model running on CUDA with float32 precision.

The following results use the small-float16 variant.

Install huggingface-cli

To download one of the .mlpackage folders to the models directory:

To download everything, skip the --include argument.

The huggingface/coreml-examples repository contains sample Swift code for DepthAnythingV2SmallF16.mlpackage and other models. See the instructions there to build the demo app, which shows how to use the model in your own Swift apps.