Instructions to use depth-anything/Depth-Anything-V2-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- DepthAnythingV2
How to use depth-anything/Depth-Anything-V2-Small with DepthAnythingV2:
# Install from https://github.com/DepthAnything/Depth-Anything-V2 # Load the model and infer depth from an image import cv2 import torch from depth_anything_v2.dpt import DepthAnythingV2 # instantiate the model model = DepthAnythingV2(encoder="vits", features=64, out_channels=[48, 96, 192, 384]) # load the weights filepath = hf_hub_download(repo_id="depth-anything/Depth-Anything-V2-Small", filename="depth_anything_v2_vits.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict).eval() raw_img = cv2.imread("your/image/path") depth = model.infer_image(raw_img) # HxW raw depth map in numpy - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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language:
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- en
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pipeline_tag: depth-estimation
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tags:
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- depth
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- relative depth
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---
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# Depth-Anything-V2-Small
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## Introduction
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Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features:
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- more fine-grained details than Depth Anything V1
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- more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
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- more efficient (10x faster) and more lightweight than SD-based models
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- impressive fine-tuned performance with our pre-trained models
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For more details, please refer to our [project page](https://depth-anything-v2.github.io/), [github](https://github.com/DepthAnything/Depth-Anything-V2) and [paper]()
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