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
Add original best PyTorch checkpoint (step 15792)
Browse files- FaceDepth_step15792.pt +3 -0
FaceDepth_step15792.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:8998c09c9fe1cddbc450f7a6ae5e57b5d95c4dff875dc3e99f4b7fd9d9514e70
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size 2682987251
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