README.md

metadata

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 — a complete port of MoGe-2 (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 app streams these weights automatically on first load. To reproduce the conversion from the original checkpoint:

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).