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---
license: mit
---
# DPT 3.1 (Swinv2 backbone)
DPT (Dense Prediction Transformer) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/MiDaS/tree/master).
Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This DPT model uses the [Swinv2](https://huggingface.co/docs/transformers/model_doc/swinv2) model as backbone and adds a neck + head on top for monocular depth estimation.
## How to use
Here is how to use this model for zero-shot depth estimation on an image:
```python
from transformers import DPTImageProcessor, DPTForDepthEstimation
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)
processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-base-384")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-base-384")
# prepare image for the model
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
```
or one can use the pipeline API:
```python
from transformers import pipeline
pipe = pipeline(task="depth-estimation", model="Intel/dpt-swinv2-base-384")
result = pipe("http://images.cocodataset.org/val2017/000000039769.jpg")
result["depth"]
``` |