Instructions to use a-ml/FaceDepth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- DepthAnythingV2
How to use a-ml/FaceDepth 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="<ENCODER>", features=<NUMBER_OF_FEATURES>, out_channels=<OUT_CHANNELS>) # load the weights filepath = hf_hub_download(repo_id="a-ml/FaceDepth", filename="depth_anything_v2_<ENCODER>.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
- Xet hash:
- e12a9c156b34df8e6d56cf4713439bc35d72eb561447688f78edd7184121924a
- Size of remote file:
- 9.84 MB
- SHA256:
- a3fac81fe387f9657e72764184d3600dbe2cdbd27ea060156cae8a0c6547dff4
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