Depth Anything (base sized model, Transformers version) Depth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Online demo is also provided. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Model description Depth Anything leverages the DPT architecture with a DINOv2 backbone. The model is trained on ~62 million images, obtaining state of the art results for both relative and absolute depth estimation. Depth Anything overview. Taken from the original paper . Intended uses & limitations You can use the raw model for tasks like zero shot depth estimation. See the model hub to look for other versions on a task that interests you. How to use Here is how to use this model to perform zero shot depth estimation: Alternatively, one can use the classes themselves: For more code examples, we refer to the documentation. BibTeX entry and citation info
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