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*This model was released on 2024-10-02 and added to Hugging Face Transformers on 2025-02-10 and contributed by [geetu040](https://github.com/geetu040).*

# DepthPro

[DepthPro](https://huggingface.co/papers/2410.02073) is a foundation model for zero-shot metric monocular depth estimation, generating high-resolution depth maps with sharpness and fine details. It uses a multi-scale Vision Transformer (ViT)-based architecture with a shared Dinov2 encoder and a DPT-like fusion stage for precise depth estimation. The model achieves metric accuracy without camera intrinsics and produces a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. Technical contributions include an efficient multi-scale vision transformer, a combined real and synthetic dataset training protocol, and state-of-the-art focal length estimation from a single image.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="depth-estimation", model="apple/DepthPro-hf", dtype="auto")
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>
<hfoption id="AutoModel">

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

image_processor = AutoImageProcessor.from_pretrained("apple/DepthPro-hf")
model = AutoModelForDepthEstimation.from_pretrained("apple/DepthPro-hf", dtype="auto")
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")

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

post_processed_output = image_processor.post_process_depth_estimation(
    outputs,
    target_sizes=[(image.height, image.width)],
)
field_of_view = post_processed_output[0]["field_of_view"]
focal_length = post_processed_output[0]["focal_length"]
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
Image.fromarray(depth.astype("uint8"))
```

</hfoption>
</hfoptions>

## DepthProConfig

[[autodoc]] DepthProConfig

## DepthProImageProcessor

[[autodoc]] DepthProImageProcessor
    - preprocess
    - post_process_depth_estimation

## DepthProImageProcessorFast

[[autodoc]] DepthProImageProcessorFast
    - preprocess
    - post_process_depth_estimation

## DepthProModel

[[autodoc]] DepthProModel
    - forward

## DepthProForDepthEstimation

[[autodoc]] DepthProForDepthEstimation
    - forward

