Instructions to use depth-anything/Depth-Anything-V2-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- DepthAnythingV2
How to use depth-anything/Depth-Anything-V2-Large 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="vitl", features=256, out_channels=[256, 512, 1024, 1024) # load the weights filepath = hf_hub_download(repo_id="depth-anything/Depth-Anything-V2-Large", filename="depth_anything_v2_vitl.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
Make sure download stats work
#2
by nielsr HF Staff - opened
README.md
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@@ -4,6 +4,7 @@ license: cc-by-nc-4.0
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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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language:
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- en
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pipeline_tag: depth-estimation
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library_name: depth-anything-v2
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tags:
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- depth
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- relative depth
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