---
title: depth-anything-v2-npu
canonical_url: "https://www.modelscope.ai/models/NexaAI/depth-anything-v2-npu"
md_url: "https://www.modelscope.ai/models/NexaAI/depth-anything-v2-npu.md"
repository: NexaAI/depth-anything-v2-npu
last_updated: 2026-02-20
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 143
stars: 0
---

# depth-anything-v2-npu

> depth-anything-v2-npu - An open-source model by NexaAI on ModelScope. This model is made for deployment on Snapdragon AIPC (Snapdragon X Elite and X2 Elite) NPU.

- **Repository**: NexaAI/depth-anything-v2-npu
- **Downloads**: 143
- **Stars**: 0
- **Last updated**: 2026-02-20

Source: https://www.modelscope.ai/models/NexaAI/depth-anything-v2-npu

---

# Depth Anything V2

This model is made for deployment on Snapdragon AIPC (Snapdragon X Elite and X2 Elite) NPU.

## Quickstart
1. **Install [NexaSDK](https://sdk.nexa.ai)** and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai)  
2. **Activate your device** with your access token:
   ```bash
   nexa config set license '<access_token>'
   ```
3. Run the model on Qualcomm NPU in one line:
   ```bash
   nexa infer NexaAI/depth-anything-v2-npu
   ```

## Model Description
**Depth Anything V2** is a foundation model for **monocular depth estimation** (predicting a depth map from a single RGB image).  
It improves on Depth Anything V1 with **finer details and stronger robustness**, while remaining **significantly more efficient** than diffusion-based depth models (often cited as ~10× faster).

Depth Anything V2 is trained using a large-scale pipeline combining **synthetic labeled depth data** and **massive real-world unlabeled imagery**, with a teacher–student strategy bridged by pseudo-labeled real images.

## Features
- **Monocular depth estimation**: dense depth map prediction from a single image.
- **Fine-grained structure**: sharper edges and better small-object depth details vs V1.
- **Robust generalization**: improved stability across diverse real-world scenes and conditions.
- **Efficient deployment**: lightweight compared to diffusion-based depth methods, enabling faster inference.
- **Multiple sizes & variants**: commonly released as Small/Base/Large (and other checkpoints), including metric fine-tunes for indoor/outdoor depth.

## Use Cases
- AR/VR & 3D effects: background segmentation, depth-aware rendering, relighting
- Robotics & navigation: obstacle understanding and scene geometry from RGB cameras
- Photo/video post-processing: synthetic bokeh, depth-based editing and compositing
- Autonomous/ADAS prototyping: monocular depth priors for perception pipelines (especially with metric fine-tunes)
- On-device deployment: optimized inference targets (e.g., model hubs and edge toolchains)

## Inputs and Outputs
**Input**:
- RGB image (single frame), typically resized/normalized per checkpoint configuration.

**Output**:
- **Relative depth map** (per-pixel depth up to scale) for general models.
- **Metric depth map** (depth in real units) for specific fine-tuned metric variants (e.g., indoor/outdoor).

## License
This repo is licensed under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution. All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications. Commercial licensing or enterprise usage requires a separate agreement. For inquiries, please contact `dev@nexa.ai`