Instructions to use LibreYOLO/LibreMoGe3l-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LibreYOLO
How to use LibreYOLO/LibreMoGe3l-depth with LibreYOLO:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
LibreMoGe3l-depth
MoGe-3 ViT-L/14 for monocular metric geometry (depth, point map, camera intrinsics, validity mask), repackaged in LibreYOLO checkpoint format.
Source
Derived from Ruicheng/moge-3-vitl model.pt
at revision 184008f877d7ad1ad4c2cd2182a9bd1f63d0e5be, the exact commit LibreYOLO pins. The source model card
declares these weights MIT.
Copyright (c) Microsoft Corporation. Code: microsoft/MoGe
at 74fbce054ebed49800de42d0ad0e83495065719a, MIT License.
The DINOv2 encoder MoGe-3 builds on is separately licensed Apache-2.0 by
Meta AI; both notices are preserved in MoGe's composite LICENSE, copied
verbatim here.
Modifications
None to the learned parameters: every tensor of the official checkpoint is
kept unchanged, and LibreYOLO checkpoint v1.0 metadata (task depth) is
added. The same tensors serve the depth and normal tasks. See
weights/convert_moge3_weights.py in the
LibreYOLO source repository.
Usage
from libreyolo import LibreYOLO
model = LibreYOLO("LibreMoGe3l-depth.pt")
result = model.predict("image.jpg")
depth = result.depth_map # metric depth in metres, inf where invalid
points = depth.point_map # (H, W, 3) camera-frame points, metres
K = depth.intrinsics # (3, 3) OpenCV pixel intrinsics
valid = depth.valid_mask # (H, W) boolean
License
MIT License. See the LICENSE and NOTICE files in
this repository.
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