---
license: mit
base_model: Ruicheng/moge-2-vitl-normal
tags:
- webgpu
- depth-estimation
- surface-normals
- monocular-depth
pipeline_tag: depth-estimation
---

# MoGe-2 WebGPU weights

Pre-converted fp16 weights for [moge-webgpu](https://github.com/lyonsno/moge-webgpu) — a complete port of [MoGe-2](https://github.com/microsoft/MoGe) (DINOv2 ViT-Large + ConvStack decoder, `Ruicheng/moge-2-vitl-normal`) from PyTorch to pure WebGPU compute shaders. No server, no WASM, no ONNX runtime.

## Files

- `weights.bin` (~660MB) — flat fp16 binary, all model tensors concatenated for direct WebGPU buffer upload
- `weights.json` — tensor manifest (names, shapes, offsets)

## Usage

The [moge-webgpu](https://github.com/lyonsno/moge-webgpu) app streams these weights automatically on first load. To reproduce the conversion from the original checkpoint:

```bash
python tools/convert_weights.py \
  --model Ruicheng/moge-2-vitl-normal \
  --output public/weights.bin \
  --dtype fp16
```

## License

MIT, matching upstream MoGe-2. Original model by Microsoft Research ([MoGe-2 paper](https://arxiv.org/abs/2507.02546)).