Depth Anything V2 (Fine tuned for Metric Depth Estimation) Transformers Version This model represents a fine tuned version of Depth Anything V2 for indoor metric depth estimation using the synthetic Hypersim datasets. The model checkpoint is compatible with the transformers library. 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 employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine tuned version for metric depth estimation was first released in this repository. Six metric depth models of three scales for indoor and outdoor scenes, respectively, were released and are available: Base Model Params Indoor (Hypersim) Outdoor (Virtual KITTI 2) : : : : : : Depth Anything V2 Small 24.8M Model Card Model Card Depth Anything V2 Base 97.5M Model Card Model Card Depth Anything V2 Large 335.3M Model Card Model Card Model description 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 ar…
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