---
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.