Depth Anything V2

Install

Depth Anything V2 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("LibreDepthAnythingV2s-depth.pt")result = model(SAMPLE_IMAGE, save=True) depth = result.depth_mapprint(depth.min, depth.max, depth.mean)

libreyolo predict model=LibreDepthAnythingV2s-depth.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreDepthAnythingV2s-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. 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. Input resolution must divide evenly by 14, the DINOv2 patch grid the DPT head builds on; LibreYOLO checks this before running and raises if it does not. See prediction for sources, streaming and result handling.

Variants

Four encoder sizes, s/b/l/g, corresponding to ViT-S/B/L/G. The checkpoint table below lists only s, b and l; no Giant checkpoint is published. All four share the same input resolution, so choosing a size trades encoder capacity, not image size. Licensing is also a factor: the Small checkpoint is Apache-2.0, while Base and Large are CC-BY-NC-4.0, see Licensing below.

Training and fine-tuning are not offered for this family. LibreDepthAnythingV2.train() raises NotImplementedError unconditionally; convert a compatible upstream checkpoint instead, with weights/convert_depth_anything_v2_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("LibreDepthAnythingV2s-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=LibreDepthAnythingV2s-depth.pt data=my-dataset.yaml

Export

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. Export lists the arguments every format accepts.

from libreyolo import LibreYOLO model = LibreYOLO("LibreDepthAnythingV2s-depth.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreDepthAnythingV2s-depth.pt format=onnxlibreyolo export model=LibreDepthAnythingV2s-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("LibreDepthAnythingV2s-depth.onnx")result = model(SAMPLE_IMAGE) print(result.depth_map.data.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{depth_anything_v2,
  title={Depth Anything V2},
  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
  journal={arXiv:2406.09414},
  year={2024}
}