mlboydaisuke / Depth-Anything-3-CoreAI

huggingface.co
Total runs: 30
24-hour runs: -2
7-day runs: -9
30-day runs: -85
Model's Last Updated: September 25 2026
depth-estimation

Introduction of Depth-Anything-3-CoreAI

Model Details of Depth-Anything-3-CoreAI

Depth Anything 3 — Core AI

The coreai-model-zoo 's first depth model. Monocular (single-image) relative depth estimation running fully on-device on Apple's Core AI runtime, as a single static .aimodel . A conversion of ByteDance's Depth Anything 3 ( depth-anything/DA3-SMALL / DA3-BASE , Apache-2.0): a DINOv2 ViT backbone + DPT-style head. Drop in an RGB image, get a depth map (and a confidence map). No NMS, no sampling — host post-processing is just a colormap.

Bundles
dir variant params dtype size M4 Max GPU
small/da3-small_float16.aimodel ViT-S 34.3M fp16 54 MB 65.7 FPS
small/da3-small_float32.aimodel ViT-S 34.3M fp32 105 MB 56.5 FPS
base/da3-base_float16.aimodel ViT-B 135.4M fp16 202 MB 26.5 FPS
base/da3-base_float32.aimodel ViT-B 135.4M fp32 402 MB 23.0 FPS

small · fp16 is the on-device hero — 54 MB, 65 FPS at 504² on an M4 Max, comfortably real-time on iPhone-class GPUs. Each .aimodel is a directory bundle ( main.mlirb + metadata.json ).

I/O contract
input : image [1, 3, 504, 504]  RGB, raw [0, 1]   (ImageNet normalization is folded into the graph)
output: depth      [1, 504, 504]  relative depth (exp-activated; larger = nearer)
        depth_conf [1, 504, 504]  confidence

Host: resize the RGB image to 504 × 504 (e.g. cv2 INTER_AREA ), feed raw [0, 1], run, then resize the depth map back to the original H × W. For display, the DA3 convention is inverse-depth → percentile 2–98 normalize → Spectral colormap.

Fidelity
  • Bit-exact conversion: the Core AI engine matches the PyTorch reference at cos 1.000000 (≤ ~1e-5 / ~1e-2 per-pixel for fp32 / fp16) on both CPU and GPU, at any fixed input shape.
  • vs the official DA3 viewer: mean Pearson r ≈ 0.98 across diverse aspect ratios (square inputs r = 1.000) — within DA3's own resolution sensitivity (its 504-vs-518 outputs differ by r ≈ 0.975–0.984).
Usage (CoreAIKit / coreai.runtime)
import coreai.runtime as rt, numpy as np
from PIL import Image

m = await rt.AIModel.load("small/da3-small_float16.aimodel",
        rt.SpecializationOptions.from_preferred_compute_unit_kind(rt.ComputeUnitKind.gpu()))
fn = m.load_function("main")

img = np.asarray(Image.open("photo.jpg").convert("RGB").resize((504, 504)))
x = (img.astype(np.float16) / 255.0).transpose(2, 0, 1)[None]   # raw [0,1], NCHW
depth = (await fn({"image": rt.NDArray(x)}))["depth"].numpy().reshape(504, 504)
Links

On-device ML / Core ML / Core AI model porting — get in touch: open an issue on the zoo .

Runs of mlboydaisuke Depth-Anything-3-CoreAI on huggingface.co

30
Total runs
-2
24-hour runs
-8
3-day runs
-9
7-day runs
-85
30-day runs

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Depth-Anything-3-CoreAI install

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