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## Model
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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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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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###
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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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[More Information Needed]
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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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[More Information Needed]
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### Results
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[More Information Needed]
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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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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 Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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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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# 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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```
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