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*This model was released on 2024-06-13 and added to Hugging Face Transformers on 2024-07-05.*

# Depth Anything V2

[Depth Anything V2](https://huggingface.co/papers/2406.09414) enhances monocular depth estimation by replacing real images with synthetic data, increasing the teacher model's capacity, and using large-scale pseudo-labeled real images to train student models. This results in finer and more robust depth predictions, offering efficiency and accuracy improvements over models based on Stable Diffusion. Available in various sizes (25M to 1.3B parameters), these models can be fine-tuned for metric depth tasks. The paper also introduces a new evaluation benchmark with precise annotations and diverse scenes to support future research.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-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("depth-anything/Depth-Anything-V2-Small-hf")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Small-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)],
)
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>

## DepthAnythingConfig

[[autodoc]] DepthAnythingConfig

## DepthAnythingForDepthEstimation

[[autodoc]] DepthAnythingForDepthEstimation
    - forward

