Depth Anything 3

Install

Depth Anything 3 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("LibreDepthAnything3l-depth.pt")result = model(SAMPLE_IMAGE, save=True) depth = result.depth_mapprint(depth.min, depth.max, depth.mean)

libreyolo predict model=LibreDepthAnything3l-depth.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreDepthAnything3l-depth.pt")result = model(SAMPLE_IMAGE) depth = result.depth_map    # DepthMap: dense (H, W), higher = closerraw = depth.data                # tensor, no metric unit or cross-image scalenormalized = depth.normalized() # rescaled to [0, 1] for visualization

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. The upstream checkpoint emits positive relative depth; LibreYOLO's network wrapper inverts it and reproduces the official sky handling so the output follows LibreYOLO's shared depth contract. 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

One size, l, at a fixed input resolution. Upstream DA3 also publishes Small and Base any-view checkpoints, a metric-depth checkpoint, and Nested and Giant checkpoints; LibreYOLO exposes none of them. Metric depth needs a different public contract than LibreYOLO's relative-inverse-depth task, and the any-view and Nested checkpoints need a multi-image camera API LibreYOLO does not offer. The Large and Giant any-view checkpoints are also CC-BY-NC-4.0 and are not referenced by any LibreYOLO download path.

Training is not offered for this family. LibreDepthAnything3.train() raises NotImplementedError unconditionally; train upstream and convert a compatible DA3MONO-LARGE checkpoint with weights/convert_depth_anything3_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("LibreDepthAnything3l-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=LibreDepthAnything3l-depth.pt data=my-dataset.yaml

Export

Export is restricted to five formats for this family: ONNX, TorchScript, ExecuTorch, TensorRT and OpenVINO. Requesting any other format raises NotImplementedError rather than attempting an unvalidated conversion. An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results, with depth_map in place of boxes.

from libreyolo import LibreYOLO model = LibreYOLO("LibreDepthAnything3l-depth.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreDepthAnything3l-depth.pt format=onnxlibreyolo export model=LibreDepthAnything3l-depth.pt format=tensorrt half=True

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("LibreDepthAnything3l-depth.onnx")result = model(SAMPLE_IMAGE) print(result.depth_map.data.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{depthanything3,
  title={Depth Anything 3: Recovering the visual space from any views},
  author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
  journal={arXiv preprint arXiv:2511.10647},
  year={2025}
}