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 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 and the
img depth 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
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.