README.md

---
license: apache-2.0
base_model: depth-anything/Depth-Anything-V2-Small
tags:
  - depth-estimation
  - monocular-depth
  - depth-anything
  - core-ai
  - apple-silicon
  - macos
library_name: swift-depth-estimator
---

# img-depth-anything — Depth Anything V2 Small for Core AI (macOS 27)

[Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2) Small (Yang, Kang, Ming,
Xu, Feng and Zhao, NeurIPS 2024; Apache 2.0) exported from the authors' own code and checkpoint
(`depth_anything_v2_vits.pth`) to an Apple Core AI `.aimodel` for on-device monocular depth. Read
by [swift-depth-estimator](https://github.com/arraypress/swift-depth-estimator) and the
[`img depth`](https://github.com/arraypress/swift-img-cli) verb.

## Files

| Path | What |
|---|---|
| `img-depth-anything-v2-small-float32.aimodel/` | Nine entry points, one per input size the authors' `Resize` (shorter side 518, multiples of 14, `lower_bound`) produces for common aspect ratios: `h518w518`, `h518w686`, `h686w518`, `h518w784`, `h784w518`, `h518w924`, `h924w518`, `h518w826`, `h826w518`. Each takes an ImageNet-normalised RGB image `[1, 3, H, W]` and returns relative inverse depth `[1, H, W]` (larger is nearer). Float32, 102 MB — the weights are shared. |

The host reproduces `infer_image`: OpenCV `INTER_CUBIC` on the image in 0…1, ImageNet mean and
standard deviation, the network, then bilinear upsampling with `align_corners=True` back to the
picture's own size. A picture whose aspect ratio maps to a size not in the list uses the nearest.

## Fidelity

Against the authors' PyTorch model on the CPU, on their demo images cropped to every exported
ratio: prepared tensors above 100 dB PSNR (the cubic resampler is exact), network outputs
111–127 dB, whole images end to end 113–130 dB.

## Use

```sh
hf download arraypress/img-depth-anything --local-dir models
img depth install models/img-depth-anything-v2-small-float32.aimodel
img depth photo.jpg        # photo-depth.png, nearer is brighter
```

Requires macOS 27 (Core AI) on Apple silicon. Converted with coreai-torch 0.4.2 / torch 2.13.