Monocular Depth Estimation with Ultralytics YOLO 🚀

[raimbekovm]

Ultralytics YOLO now supports monocular depth estimation — a dense prediction task that estimates how far every pixel in an image is from the camera, using a single RGB frame.

Unlike stereo rigs or LiDAR, monocular depth estimation needs no second camera, no extra sensor, and no calibration setup. The result is an H×W float map where each pixel value is the predicted distance in meters, aligned to the input image.

That makes it a strong fit for:

🤖 Robotics and drones — Obstacle distance and free space from a single onboard camera.
🚗 Autonomous driving — Distance to vehicles, pedestrians, and road structure at pixel level.
🥽 AR/VR — Correct occlusion and object placement grounded in real scene geometry.
🏗️ 3D reconstruction — Single images turned into point clouds and spatial layouts.

On a Tesla T4 under TensorRT fp16, YOLO26n-depth runs in 2.73 ms — 7.7× faster than Depth Anything V2 Smat YOLO26x-depth stays ahead of it at 13.57 ms.

from ultralytics import YOLO

model = YOLO("yolo26n-depth.pt")
result = model("https://ultralytics.com/images/bus.jpg")[0]

depth = result.depth.data.cpu().numpy()  # (H, W) float32, meters

Learn more ➡️ https://docs.ultralytics.com/tasks/depth

[UltralyticsAssistant]

👋 Hello @raimbekovm, thank you for sharing this exciting monocular depth estimation update with Ultralytics 🚀! We recommend visiting the Docs for new users, where you can find many Python and CLI usage examples and answers to common questions.

If you encounter a 🐛 bug while using depth estimation, please provide a minimum reproducible example to help us investigate it.

If you have a custom training ❓ question, please provide as much information as possible, including dataset image examples and training logs, and verify that you are following our Tips for Best Training Results.

This is an automated response 🤖, and an Ultralytics engineer will also assist you soon.

Join the Ultralytics community where it suits you best. For real-time chat, head to Discord 🎧. Prefer in-depth discussions? Check out Discourse. Or dive into threads on our Subreddit to share knowledge with the community.

Upgrade

Upgrade to the latest ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8 to verify your issue is not already resolved in the latest version:

pip install -U ultralytics

Environments

YOLO may be run in any of the following up-to-date verified environments, with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled:

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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