--- license: mit tags: - vision pipeline_tag: depth-estimation --- # ZoeDepth (fine-tuned on NYU) ZoeDepth model fine-tuned on the NYU dataset. It was introduced in the paper [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://arxiv.org/abs/2302.12288) by Shariq et al. and first released in [this repository](https://github.com/isl-org/ZoeDepth). ZoeDepth extends the [DPT](https://huggingface.co/docs/transformers/en/model_doc/dpt) framework for metric (also called absolute) depth estimation, obtaining state-of-the-art results. Disclaimer: The team releasing ZoeDepth did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ZoeDepth adapts [DPT](https://huggingface.co/docs/transformers/en/model_doc/dpt), a model for relative depth estimation, for so-called metric (also called absolute) depth estimation. This means that the model is able to estimate depth in actual metric values. drawing ZoeDepth architecture. Taken from the original paper. ## Intended uses & limitations You can use the raw model for tasks like zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=Intel/zoedepth) to look for other versions on a task that interests you. ### How to use The easiest is to leverage the pipeline API which abstracts away the complexity for the user: ```python from transformers import pipeline from PIL import Image import requests # load pipe depth_estimator = pipeline(task="depth-estimation", model="Intel/zoedepth-nyu") # load image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) # inference outputs = depth_estimator(image) depth = outputs.depth ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/zoedepth.html#). ### BibTeX entry and citation info ```bibtex @misc{bhat2023zoedepth, title={ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth}, author={Shariq Farooq Bhat and Reiner Birkl and Diana Wofk and Peter Wonka and Matthias Müller}, year={2023}, eprint={2302.12288}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```