---
license: cc-by-nc-4.0
base_model: depth-anything/DA3-GIANT
tags:
  - coreml
  - depth-estimation
  - depth-anything-3
pipeline_tag: depth-estimation
---

# DA3-GIANT — CoreML (.mlpackage) for monocular depth

A precompiled **Core ML** conversion of **Depth Anything 3 — DA3-GIANT** (the full
ViT-g / 1.15B model), exposing a single-image **relative-depth** output for macOS/iOS.

- **Input:** `image`, RGB, **504×504**, [0,1] (CoreML `ImageType`; ImageNet norm baked in).
- **Output:** `depth`, shape **(1, 504, 504)**, single-channel relative depth.
- **Weights:** FP16, ~2.2 GB. Only the **backbone → depth head** is converted (camera / sky /
  Gaussian-splat heads bypassed).
- **Conversion notes:** the full DA3-GIANT backbone uses RoPE + multi-view camera tokens +
  **qk-norm** + SwiGLU FFN. Five things were handled for coremltools: the four DA3-LARGE
  RoPE/cam-token/meshgrid rewrites, plus a converter-side cast shim (numpy 2.x refuses
  `int()` on size-1 non-0-dim arrays, which breaks the const-cast of `H//patch_size`).
  Single-image behaviour is unchanged.

This is the highest-capacity DA3 depth variant, and correspondingly the slowest at inference —
for real-time monocular depth the smaller
[DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE) /
[DA3-LARGE](https://huggingface.co/sdkv2/DA3-LARGE-CoreML) are usually the better trade-off.

## License & attribution

Derived from **[depth-anything/DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT)**
(Depth Anything 3, arXiv:2511.10647), **CC-BY-NC-4.0**. Released under the same license:
**attribution required, non-commercial use only.** For commercial use, see the Apache-2.0
[depth-anything/DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE).