Ultralytics now supports monocular depth estimation as a complete task, sitting next to detect, segment, pose and classify.
The latest release adds a new model family, yolo26n/s/m/l/x-depth, which uses a DPT-style head that fuses multi-scale YOLO26 features to produce a dense per-pixel depth map in meters.
Why unbounded log-depth
The depth head predicts unbounded log-depth instead of the more common sigmoid times max-depth approach. A bounded head bakes a fixed ceiling into the architecture, so anything beyond that ceiling gets clipped.
That becomes a problem once you mix indoor data (usually under 10m) with outdoor driving data like KITTI (up to 80m). We found that the bounded head plateaus after the first epoch on mixed-range data, while the log-depth head keeps improving throughout training.
On the released benchmarks the biggest gains show up exactly where you'd expect: longer-range outdoor sets like KITTI and ETH3D 🚗, while indoor accuracy stays about the same 🏠.
Full workflow support
Training, validation, prediction and export are all supported through the same Python API and CLI you already use for other tasks.
There's also a model.calibrate() method that is provided for closed-form scale calibration on a small labeled split (around 100 images is enough), with no gradient training involved, so it won't touch the relative depth structure the model already learned. Use it when the shape of the predicted depth looks right but the absolute values are off for your camera.
The released checkpoints already ship with a global calibration fit on the pretraining mix of about 2.19 million images. model.calibrate() is there for when you want to replace that with something specific to your own setup.
Example prediction
from ultralytics import YOLO
model = YOLO("yolo26n-depth.pt")
results = model("image.jpg")
depth_map = results[0].depth.data.cpu().numpy() # shape (H, W), values in meters
Same thing works from the CLI with yolo depth train, yolo depth val and yolo depth predict.
Export to ONNX, TensorRT, CoreML and the other usual formats is supported as well 🚀, same workflow as the other tasks.
Check out the docs to learn more: https://docs.ultralytics.com/tasks/depth
