| --- |
| license: mit |
| library_name: libreyolo |
| pipeline_tag: depth-estimation |
| tags: |
| - image-to-image |
| - surface-normals |
| - moge |
| --- |
| |
| # LibreMoGe3l-normal |
|
|
| MoGe-3 ViT-L/14 for surface-normal estimation, repackaged in LibreYOLO checkpoint format. |
|
|
| ## Source |
|
|
| Derived from [Ruicheng/moge-3-vitl](https://huggingface.co/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](https://github.com/microsoft/MoGe/tree/74fbce054ebed49800de42d0ad0e83495065719a) |
| 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 `normal`) is |
| added. The same tensors serve the `depth` and `normal` tasks. See |
| `weights/convert_moge3_weights.py` in the |
| [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo). |
|
|
| ## Usage |
|
|
| ```python |
| from libreyolo import LibreYOLO |
| |
| model = LibreYOLO("LibreMoGe3l-normal.pt") |
| result = model.predict("image.jpg") |
| normals = result.normals # (H, W, 3) OpenCV-frame unit normals |
| ``` |
|
|
| ## License |
|
|
| MIT License. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in |
| this repository. |
|
|
