MiDaS

Install

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

pip install libreyolo

Predict

MiDaS is the one depth family LibreYOLO does not republish on its own Hugging Face organization. Requesting a checkpoint by its LibreYOLO filename downloads the matching official asset directly from the isl-org/MiDaS GitHub releases, checks it against a pinned SHA-256, and wraps it with LibreYOLO's checkpoint metadata before first use; later runs reuse the cached local file. See Licensing for why.

from libreyolo import LibreYOLO, SAMPLE_IMAGE # Not on disk yet: LibreYOLO downloads it from the official isl-org/MiDaS# GitHub release and checks it against a pinned SHA-256 before use.model = LibreYOLO("LibreMiDaSl-depth.pt")result = model(SAMPLE_IMAGE, save=True) depth = result.depth_mapprint(depth.min, depth.max, depth.mean)

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

from libreyolo import LibreYOLO, SAMPLE_IMAGE # EfficientNet-Lite3 encoder, smaller and faster than the DPT-Large l size.model = LibreYOLO("LibreMiDaSs-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 variants with different encoders, not just different scales of the same one. s is MiDaS v2.1 Small, an EfficientNet-Lite3 encoder. l is DPT-Large, a ViT-L/16 encoder with the DPT decoder MiDaS introduced for dense prediction. They also preprocess differently: s uses an upper-bound aspect resize with ImageNet mean/std normalization, l uses a minimal aspect resize with mean and std of 0.5. Pick s for a lighter CNN, l for the transformer decoder's accuracy.

Training is not offered for this family. LibreMiDaS.train() raises NotImplementedError unconditionally.

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("LibreMiDaSl-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=LibreMiDaSl-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.

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

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

Licensing

Citation

@ARTICLE {Ranftl2022,
    author  = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun",
    title   = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
    journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
    year    = "2022",
    volume  = "44",
    number  = "3"
}

@article{Ranftl2021,
	author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
	title     = {Vision Transformers for Dense Prediction},
	journal   = {ICCV},
	year      = {2021},
}