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  ---
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- library_name: transformers
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- tags: []
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
 
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- ### Direct Use
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
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- [More Information Needed]
 
 
 
 
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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+ license: mit
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+ tags:
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+ - vision
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+ pipeline_tag: depth-estimation
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  ---
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+ # ZoeDepth (fine-tuned on KITT)
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+ ZoeDepth model fine-tuned on the KITTI 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).
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+ 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.
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+ 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.
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+ ## Model description
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+ 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.
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+ This means that the model is able to estimate depth in actual metric values.
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+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/zoedepth_architecture_bis.png"
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+ alt="drawing" width="600"/>
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+ <small> ZoeDepth architecture. Taken from the <a href="https://arxiv.org/abs/2302.12288">original paper.</a> </small>
 
 
 
 
 
 
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+ ## Intended uses & limitations
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+ 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
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+ other versions on a task that interests you.
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+ ### How to use
 
 
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+ The easiest is to leverage the pipeline API which abstracts away the complexity for the user:
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+ ```python
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+ from transformers import pipeline
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+ from PIL import Image
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+ import requests
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+ # load pipe
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+ depth_estimator = pipeline(task="depth-estimation", model="Intel/zoedepth-kitti")
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+ # load image
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+ url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ # inference
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+ outputs = depth_estimator(image)
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+ depth = outputs.depth
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+ ```
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+ For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/zoedepth.html#).
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+ ### BibTeX entry and citation info
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+ ```bibtex
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+ @misc{bhat2023zoedepth,
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+ title={ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth},
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+ author={Shariq Farooq Bhat and Reiner Birkl and Diana Wofk and Peter Wonka and Matthias Müller},
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+ year={2023},
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+ eprint={2302.12288},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ```