ZipDepth

Install

ZipDepth needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreZipDepthb-depth.pt")result = model(SAMPLE_IMAGE, save=True) depth = result.depth_mapprint(depth.min, depth.max, depth.mean)

libreyolo predict model=LibreZipDepthb-depth.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE # Same encoder, an unfold-free upsampling head for compilers that lack# gather/unfold support. Output is visually equivalent to the b checkpoint.model = LibreYOLO("LibreZipDepthbnpu-depth.pt")result = model(SAMPLE_IMAGE, save=True)

result.depth_map carries a dense relative inverse-depth map: higher values mean closer to the camera, and the values have no metric unit or cross-image scale. save=True writes a colormapped visualization of that map to disk; Results.plot() does not cover this family, since it is defined for surface normals and edges only. See prediction for sources, streaming and result handling.

Variants

Two checkpoints, both the same encoder capacity, differing only in the trained upsampling head. b uses convex upsampling and runs on GPU or CPU. bnpu swaps in an unfold-free decoder for NPU and edge compilers that lack gather/unfold support; its output is documented as visually equivalent to b. Pick bnpu when the export target is a constrained runtime, b otherwise.

Both checkpoints were distilled from Depth Anything V2 Large pseudo-labels, so this family is the compact, edge-oriented tier of LibreYOLO's depth task, alongside the larger Depth Anything V2 encoders.

Training is not offered for this family. LibreZipDepth.train() raises NotImplementedError unconditionally: the upstream recipe distills pseudo-labels over a large image set that is not reproducible as a LibreYOLO training run. Train upstream at fabiotosi92/ZipDepth and convert the result with weights/convert_zipdepth_weights.py.

Validate

val() runs the shared depth validator: it aligns each prediction to its ground truth with a per-image least-squares scale and shift, then reports the standard zero-shot relative-depth metrics, AbsRel, RMSE and the three delta thresholds.

from libreyolo import LibreYOLO model = LibreYOLO("LibreZipDepthb-depth.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/abs_rel"])print(metrics["metrics/rmse"])print(metrics["metrics/delta1"])

libreyolo val model=LibreZipDepthb-depth.pt data=my-dataset.yaml

Export

Export follows a fixed-resolution dense contract: the source image is stretch-resized to the exported canvas, and the returned depth map is resized back to the original canvas afterward. An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .ncnn file behaves like a checkpoint and returns the same Results, with depth_map in place of boxes.

from libreyolo import LibreYOLO model = LibreYOLO("LibreZipDepthb-depth.pt")model.export(format="onnx")model.export(format="ncnn")

libreyolo export model=LibreZipDepthb-depth.pt format=onnxlibreyolo export model=LibreZipDepthbnpu-depth.pt format=ncnn

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreZipDepthb-depth.onnx")result = model(SAMPLE_IMAGE) print(result.depth_map.data.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@inproceedings{tosi2026zipdepth,
  title     = {ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device},
  author    = {Tosi, Fabio and Bartolomei, Luca and Poggi, Matteo and Mattoccia, Stefano},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}