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This tutorial compares Ultralytics YOLO26 Depth with Depth Anything V2 for monocular depth estimation, then walks through a Python script that processes video frames. The speaker explains how a single RGB image produces a per-pixel depth map and discusses combining that output with object detection or segmentation.
The documentation walkthrough distinguishes relative scene depth from absolute distance. The speaker explains that calibration needs ground-truth depth and a labeled data split to fit the scale for a camera or dataset. The documentation example uses model.calibrate and saves the calibrated model. This matters when an application needs distances in meters rather than relative depth relationships.
The code loads YOLO through Ultralytics and Depth Anything V2 through Hugging Face Transformers. It covers preprocessing, output resizing, colored overlays, video writing and FPS measurements. Setup includes installing or upgrading ultralytics for depth support and installing transformers for the comparison model; model weights download automatically.
For developers evaluating local AI vision workloads, the MacBook CPU demo gives a specific performance example: the speaker reports roughly 11-12 FPS with YOLO26 nano and about 1.23 FPS with Depth Anything V2 small. An earlier comparison reports roughly twice the speed with YOLO26 medium. These are results from the presented tests, not hardware-independent benchmarks.
