Try the Model

Use the widget below to experiment with Depth Anything V2. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

Depth Anything V2 is a state-of-the-art monocular depth estimation model, meaning it can predict depth information from a single image, without needing multiple cameras. It achieves this by training a powerful "teacher" model on synthetic images and then using it to generate pseudo-labels for a large set of real-world unlabeled images. These pseudo-labels are then used to train "student" models, resulting in a model that generalizes well to various scenes and conditions.

You can use Depth-Anything-V2-Small to estimate the depth of objects in images, creating a depth map where:

- Each pixel's value represents its relative distance from the camera

- Lower values (darker colors) indicate closer objects- Higher values (lighter colors) indicate further objects

To learn how to deploy Depth Anything V2, read our deployment guide.

Here is an example of a depth mask from the model:

Depth Anything V2 License

Depth Anything V2

is licensed under a

Apache 2.0

license.

Performance

Deploy a Depth Anything V2 API

You can use Roboflow Inference to deploy a

Depth Anything V2

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

Label Data Automatically with Depth Anything V2

You can automatically label a dataset using Depth Anything V2 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use Depth Anything V2 to train a computer vision model.

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