Depth Anything Vits14

--- tags: - depth_anything - depth-estimation --- # Depth Anything model, small The model card for our paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891). You may also try our [demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) and visit our [project page](https://depth-anything.github.io/). ## Installation First, install the Depth Anything package: ``` git clone https://github.com/LiheYoung/Depth-Anything cd Depth-Anything pip install -r requirements.txt ``` ## Usage Here's how to run the model: ```python import numpy as np from PIL import Image import cv2 import torch from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet from torchvision.transforms import Compose model = DepthAnything.from_pretrained("LiheYoung/depth_anything_vitl14") transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) image = Image.open("...") image = np.array(image) / 255.0 image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0) depth = model(image) ```

Source context: 3731 downloads · 9 likes · Pipeline depth-estimation · Library transformers · Repo LiheYoung/depth_anything_vits14