Dpt Swinv2 Base 384

🚀 DPT 3.1 (Swinv2 backbone)

The DPT (Dense Prediction Transformer) model is trained on 1.4 million images for monocular depth estimation, offering high - precision depth prediction capabilities.

🚀 Quick Start

The DPT (Dense Prediction Transformer) model is trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first released in this repository.

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.

✨ Features

This DPT model uses the Swinv2 model as backbone and adds a neck + head on top for monocular depth estimation.

💻 Usage Examples

Basic Usage

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)

Advanced Usage

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"]

📄 License

This model is released under the MIT license.