ZoeDepth (fastforks)
ZoeDepth (Bhat et al. 2023): metric monocular depth from a MiDaS v3.1 BEiT-L/16 core with metric-bins heads (NYU indoor, KITTI outdoor) chosen by a learned domain router (ZoeD_M12_NK).
This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: https://github.com/kleinicke/fastforks. Kernels for the kernels backend: https://huggingface.co/fastforks/kernels. Original code: https://github.com/isl-org/ZoeDepth at d87f17b.
Task: depth. Tier: experimental. Outputs: depth. Backends: portable, kernels.
Input: one RGB image. Output: a depth or inverse-depth map. kind (a card field, result.kind) says what the numbers mean: metric = metres; scale_invariant, affine_invariant and affine_invariant_inverse are relative (unknown scale or shift, not metres). Metric models that use the focal length take model(image, intrinsics=K) (K [3, 3], pixels of the input): metric3d-vit-large and da3metric-large need it for correct metres (da3metric-large: metres = output x focal / 300; without intrinsics= a 300 px focal length is assumed, wrong for a real camera). unidepth-v2-vitl14, unik3d-vitl, depth-pro and MoGe estimate the camera themselves (given intrinsics= replace fx); zoedepth-nk and the relative models (da3mono-large too) take none.
Variants
Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in card.toml.
| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
zoedepth-nk@1 |
ZoeD_M12_NK.pt | 5d26daa15d42 |
MIT (assumed from code) | yes |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: LICENSE.
Code licence: MIT; the authors do not state a separate weights licence.
Training data: MiDaS v3.1 BEiT-L relative-depth pre-training on 12 datasets, then metric fine-tuning on NYU Depth V2 and KITTI (ZoeDepth paper, arXiv 2302.12288).
Licence evidence: https://github.com/isl-org/ZoeDepth/blob/d87f17b2f5fdcb174cf4fb115491f4a6c60de152/LICENSE (weights are release assets of the same repository)
Input size
any H x W >= 8, resized or padded inside the model
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): zoedepth-nk 3000x4000 (2.4 GiB peak allocated, 2.6 reserved)
Speed and memory
Depth, Middlebury 2014 half, 994x1482
| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
|---|---|---|---|---|---|---|---|---|---|
zoedepth-nk |
408.0 | 271.2 | 123.96 | 88.87 | 3.1x | 1.4x | 2040 / 2146 | 2037 / 2124 | 1771 / 1960 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
zoedepth-nk |
8.35 | 1.92 | 0.33† | 1.53 | 0.28 | 0.12 |
torch.compile and TensorRT
| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
|---|---|---|---|---|---|---|---|---|---|---|
zoedepth-nk |
994x1482 | 123.96 | fails: Dynamo: numpy size in model code | – | fails: ONNX export: torch.export failed | – | 88.87 | 2037 / 2124 | – | – |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
zoedepth-nk |
portable | 107.5 ms / 1857 / 1966 | 147.2 ms / 2046 / 2184 | 147.6 ms / 2107 / 2220 | 113.0 ms / 2451 / 2658 |
zoedepth-nk |
kernels | 74.9 ms / 1548 / 1802 | 105.6 ms / 1847 / 2138 | 106.2 ms / 1907 / 2230 | 80.3 ms / 2142 / 2486 |
Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
Resizes to its working resolution: time depends on the aspect ratio, not the input pixels.
Hardware, protocol and caveats: benchmarks (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or (fp32) where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
Accuracy
On public datasets
AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original | portable | kernels | note |
|---|---|---|---|---|---|
zoedepth-nk |
NYUv2 test, AbsRel / delta1 | 0.0532 / 0.9737 | 0.0528 / 0.9743 | 0.0528 / 0.9743 | |
zoedepth-nk |
iBims-1, AbsRel / delta1 | 0.0669 / 0.9545 | 0.0668 / 0.9546 | 0.0668 / 0.9546 | |
zoedepth-nk |
ETH3D two-view train left, AbsRel / delta1 | 0.1139 / 0.8648 | 0.1130 / 0.8653 | 0.1130 / 0.8654 | possibly in the training data |
zoedepth-nk |
Middlebury 2014 trainingH left, AbsRel / delta1 | 0.0449 / 0.9860 | 0.0449 / 0.9860 | 0.0449 / 0.9860 | possibly in the training data |
Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-10-27.
Parity with the original code
Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1 % and p99 <= 3 % of the reference's median |value| (absolute error of the output depth).
Limitations
- torch.compile(fullgraph=True) and torch.export are not supported: graph break in the vendored MiDaS core call and data-dependent NYU / KITTI head routing (documented xfail).
- Inputs below 5 px per side raise InputError when pad_input is on.
Usage
from fastforks import depth
model = depth.load("zoedepth-nk") # mode="auto": the preferred backend from recorded measurements
result = model("room.jpg")
result.depth # [H, W] float32 tensor; result.kind says what the maps mean
# models that take a camera: model("room.jpg", intrinsics=K), K [3, 3] in pixels
result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
Install with pip install fastforks (see https://github.com/kleinicke/fastforks). Other variants: zoedepth-nk.
Kernels backend
depth.load("zoedepth-nk", mode="kernels") runs our CUDA kernels (cudnn_hacc). It needs a prebuilt kernel for your GPU architecture (downloaded from https://huggingface.co/fastforks/kernels on first use) and a recent NVIDIA driver; if there is none it raises BackendUnavailableError with the reason and never falls back silently. mode="auto" picks kernels when it can run and logs the choice.