ZipDepth base with the NPU decoder: a ~6M-parameter reparameterizable CNN
(RepVGG encoder, SPPF + cross-scale neck, FPN decoder) for zero-shot relative
monocular depth estimation, repackaged for LibreYOLO. This checkpoint carries
a separately trained unfold-free convex-upsampling head for NPU/edge compilers
that lack gather/unfold support; use it when exporting to constrained runtimes.
The standard GPU/CPU checkpoint is
LibreZipDepthb-depth.
1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibreZipDepthbnpu-depth.pt") # auto-downloads from this repo
4results = model.predict("image.jpg", save=True)
5depth = results[0].depth_map.data # (H, W) relative inverse depth
6model.export(format="onnx") # unfold-free fixed-resolution graph
Outputs follow LibreYOLO's depth task contract: a dense float map on the
original image canvas, higher values mean closer to the camera, no metric unit.
Derived from
fabiotosi92/ZipDepth
(
checkpoints/zipdepth_base_npu.pth) at commit
6b96f4d205f8a2e5377e81c1b74cc99a47f6693a (ECCV 2026, University of Bologna).
Copyright (c) 2026 Fabio Tosi. Licensed under the MIT License.
LibreYOLO checkpoint-schema metadata wrap only. Learned parameters are
byte-identical to upstream. See
weights/convert_zipdepth_weights.py in the
LibreYOLO source repository.
Upstream trained these weights by distilling pseudo-labels produced by Depth
Anything V2 Large (a CC-BY-NC-4.0 checkpoint) over ~14M images from 17 public
datasets. The MIT grant on the resulting student weights is the upstream
authors' published licensing position; the lineage is documented here for
transparency.
MIT License. See the
LICENSE and
NOTICE files in
this repository.
