Reliquary Depth Anything v2 Large
Depth Anything V2 Large (ViT-L, 335M params) ONNX export. Highest-quality tier for Reliquary's Heightmap Generator.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 1,84 MB
- Modelo base
- depth-anything/Depth-Anything-V2-Large
- Versión
- vRevision 9b66a24
- Actualizado
- 10 oct 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
reliquary-depth-anything-v2-large
Depth Anything V2 Large (ViT-L, ~335M params) ONNX export. Highest-quality tier for Reliquary's Heightmap Generator.
Source
ONNX re-export of the upstream model — original credits to the authors:
- Repo: https://github.com/DepthAnything/Depth-Anything-V2
- Paper: "Depth Anything V2" — Yang, Kang, Huang, Xu, Feng, Zhao, NeurIPS 2024.
- Pretrained weights origin: HuggingFace
depth-anything/Depth-Anything-V2-Large
License
Inherited from upstream — the Large variant is CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial). Non-commercial use only. Reliquary itself is non-commercial; downstream commercial users must either switch to the Small variant (Apache-2.0) or obtain a commercial license from the upstream authors.
Usage in Reliquary
This model is consumed by Reliquary, a browser-based PBR texture editor. The ONNX file is loaded via ONNX Runtime Web in the browser — no Python or server-side inference.
- Runtime integration point:
src/lib/ai/depthAnything.ts (variant picker) - Input shape / format:
[N, 3, H, W]— RGB normalised, H/W multiples of 14. - Output shape / format:
[N, 1, H, W]— relative depth, larger = closer.
Files
depth_anything_v2_large.onnx— ONNX graph (1.8 MB)depth_anything_v2_large.onnx.data— external weights (1.2 GB, present because the model exceeds the ONNX protobuf size limit)README.md— this file
Disclaimer
This is a re-export from the original PyTorch checkpoint to a browser-deployable ONNX format with dynamic input axes. The model weights, architecture, and training methodology are entirely the work of the original authors cited above. This repo is a hosting convenience for Reliquary's browser-side ML — it does NOT modify the model.
For the full conversion pipeline (including version-compat shims for
PyTorch 2.6+, opset selection, dynamic-axes details), see Reliquary's
PYTHON_CONVERSIONS_GUIDE.md.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: cc-by-nc-4.0 library_name: onnx tags: - onnx - reliquary - depth-estimation pipeline_tag: depth-estimation base_model: depth-anything/Depth-Anything-V2-Large --- # reliquary-depth-anything-v2-large Depth Anything V2 Large (ViT-L, ~335M params) ONNX export. Highest-quality tier for Reliquary's Heightmap Generator. ## Source ONNX re-export of the upstream model — original credits to the authors: - **Repo:** [https://github.com/DepthAnything/Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2) - **Paper:** "Depth Anything V2" — Yang, Kang, Huang, Xu, Feng, Zhao, NeurIPS 2024. - **Pretrained weights origin:** HuggingFace `depth-anything/Depth-Anything-V2-Large` ## License Inherited from upstream — the Large variant is **CC-BY-NC-4.0** (Creative Commons Attribution-NonCommercial). **Non-commercial use only.** Reliquary itself is non-commercial; downstream commercial users must either switch to the Small variant (Apache-2.0) or obtain a commercial license from the upstream authors. ## Usage in Reliquary This model is consumed by [**Reliquary**](https://github.com/MarekBarski/-reliquary), a browser-based PBR texture editor. The ONNX file is loaded via ONNX Runtime Web in the browser — no Python or server-side inference. - **Runtime integration point:** `src/lib/ai/depthAnything.ts (variant picker)` - **Input shape / format:** `[N, 3, H, W]` — RGB normalised, H/W multiples of 14. - **Output shape / format:** `[N, 1, H, W]` — relative depth, larger = closer. ## Files - `depth_anything_v2_large.onnx` — ONNX graph (1.8 MB) - `depth_anything_v2_large.onnx.data` — external weights (1.2 GB, present because the model exceeds the ONNX protobuf size limit) - `README.md` — this file ## Disclaimer This is a re-export from the original PyTorch checkpoint to a browser-deployable ONNX format with dynamic input axes. The model weights, architecture, and training methodology are entirely the work of the original authors cited above. This repo is a hosting convenience for Reliquary's browser-side ML — it does NOT modify the model. For the full conversion pipeline (including version-compat shims for PyTorch 2.6+, opset selection, dynamic-axes details), see Reliquary's [`PYTHON_CONVERSIONS_GUIDE.md`](https://github.com/MarekBarski/-reliquary/blob/main/scripts/PYTHON_CONVERSIONS_GUIDE.md).
Source context: 0 downloads · 1 likes · Pipeline depth-estimation · Library onnx · Repo Wszelkiebro/reliquary-depth-anything-v2-large
Source context: 0 downloads · 0 likes · Pipeline depth-estimation · Library onnx · Repo Wszelkiebro/reliquary-depth-anything-v2-large
