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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)).
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