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# ZoeDepth

## Overview

The ZoeDepth model was proposed in [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://arxiv.org/abs/2302.12288) by Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller. ZoeDepth extends the [DPT](dpt) framework for metric (also called absolute) depth estimation. ZoeDepth is pre-trained on 12 datasets using relative depth and fine-tuned on two domains (NYU and KITTI) using metric depth. A lightweight head is used with a novel bin adjustment design called metric bins module for each domain. During inference, each input image is automatically routed to the appropriate head using a latent classifier.

The abstract from the paper is the following:

*This paper tackles the problem of depth estimation from a single image. Existing work either focuses on generalization performance disregarding metric scale, i.e. relative depth estimation, or state-of-the-art results on specific datasets, i.e. metric depth estimation. We propose the first approach that combines both worlds, leading to a model with excellent generalization performance while maintaining metric scale. Our flagship model, ZoeD-M12-NK, is pre-trained on 12 datasets using relative depth and fine-tuned on two datasets using metric depth. We use a lightweight head with a novel bin adjustment design called metric bins module for each domain. During inference, each input image is automatically routed to the appropriate head using a latent classifier. Our framework admits multiple configurations depending on the datasets used for relative depth pre-training and metric fine-tuning. Without pre-training, we can already significantly improve the state of the art (SOTA) on the NYU Depth v2 indoor dataset. Pre-training on twelve datasets and fine-tuning on the NYU Depth v2 indoor dataset, we can further improve SOTA for a total of 21% in terms of relative absolute error (REL). Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/zoedepth_architecture_bis.png"
alt="drawing" width="600"/>

<small> ZoeDepth architecture. Taken from the <a href="https://arxiv.org/abs/2302.12288">original paper.</a> </small>

This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/isl-org/ZoeDepth).

## Usage tips

- ZoeDepth is an absolute (also called metric) depth estimation model, unlike DPT which is a relative depth estimation model. This means that ZoeDepth is able to estimate depth in metric units like meters.

The easiest to perform inference with ZoeDepth is by leveraging the [pipeline API](../main_classes/pipelines.md):

```python
>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> pipe = pipeline(task="depth-estimation", model="Intel/zoedepth-nyu-kitti")
>>> result = pipe(image)
>>> depth = result["depth"]
```

Alternatively, one can also perform inference using the classes:

```python
>>> from transformers import AutoImageProcessor, ZoeDepthForDepthEstimation
>>> import torch
>>> import numpy as np
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> image_processor = AutoImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti")
>>> model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-kitti")

>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")

>>> with torch.no_grad():   
...     outputs = model(inputs)

>>> # interpolate to original size and visualize the prediction
>>> ## ZoeDepth dynamically pads the input image. Thus we pass the original image size as argument
>>> ## to `post_process_depth_estimation` to remove the padding and resize to original dimensions.
>>> post_processed_output = image_processor.post_process_depth_estimation(
...     outputs,
...     source_sizes=[(image.height, image.width)],
... )

>>> predicted_depth = post_processed_output[0]["predicted_depth"]
>>> depth = (predicted_depth - predicted_depth.min()) / (predicted_depth.max() - predicted_depth.min())
>>> depth = depth.detach().cpu().numpy() * 255
>>> depth = Image.fromarray(depth.astype("uint8"))
```

<Tip>
<p>In the <a href="https://github.com/isl-org/ZoeDepth/blob/edb6daf45458569e24f50250ef1ed08c015f17a7/zoedepth/models/depth_model.py#L131">original implementation</a> ZoeDepth model performs inference on both the original and flipped images and averages out the results. The <code>post_process_depth_estimation</code> function can handle this for us by passing the flipped outputs to the optional <code>outputs_flipped</code> argument:</p>
<pre><code class="language-Python">&gt;&gt;&gt; with torch.no_grad():   
...     outputs = model(pixel_values)
...     outputs_flipped = model(pixel_values=torch.flip(inputs.pixel_values, dims=[3]))
&gt;&gt;&gt; post_processed_output = image_processor.post_process_depth_estimation(
...     outputs,
...     source_sizes=[(image.height, image.width)],
...     outputs_flipped=outputs_flipped,
... )
</code></pre>
</Tip>

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ZoeDepth.

- A demo notebook regarding inference with ZoeDepth models can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ZoeDepth). 🌎

## ZoeDepthConfig

[[autodoc]] ZoeDepthConfig

## ZoeDepthImageProcessor

[[autodoc]] ZoeDepthImageProcessor
    - preprocess

## ZoeDepthForDepthEstimation

[[autodoc]] ZoeDepthForDepthEstimation
    - forward