# DepthAnything/Depth-Anything-V2

[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation

Repository: https://github.com/DepthAnything/Depth-Anything-V2
Canonical: https://ross.abutalabs.com/products/depth-anything-v2
Homepage: https://depth-anything-v2.github.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: monocular-depth-estimation
Last push: 2026-03-24T10:59:06+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 73, release rhythm 35, longevity 57
- inputs: {"age_days": 811, "days_push": 162, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8709, forks 898 (observed 2026-08-28T04:10:24.338669+00:00)

## What it is
Depth Anything V2 is a foundation model for monocular depth estimation, trained on 595K synthetic labeled images and 62M+ real unlabeled images. It provides relative and metric depth models in multiple sizes, with faster inference and finer detail than V1 and diffusion-based alternatives.

## Use cases
- estimate depth from a single image
- generate depth maps from photos
- run monocular depth estimation in python
- metric depth estimation for indoor and outdoor scenes
- fine-tune a depth estimation model on custom data
- fast depth estimation for video frames

## When to choose
- you need accurate, fast monocular depth estimation from single images
- you want lightweight models (24.8M-335M params) instead of slow diffusion-based depth models
- you need both relative and metric depth models at multiple scales
- you want Hugging Face Transformers or Core ML integration

## When to avoid
- you need temporally consistent depth for long videos (use Video Depth Anything instead)
- you need 4K metric depth with LiDAR prompting (use Prompt Depth Anything)
- you need stereo or multi-view depth estimation
- you cannot run GPU inference

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, computer-vision, image-processing, transformers
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: monocular-depth-estimation, depth-estimation, foundation-model, metric-depth, pretrained-models, neurips-2024, gpu

## Member repositories
- DepthAnything/Depth-Anything-V2 (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:24.338669+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:25:22.465191+00:00, confidence not recorded.
  - readme: https://github.com/DepthAnything/Depth-Anything-V2 (fetched 2026-08-28T04:10:24.338669+00:00, sha d2dae1b21f42)
  - homepage: https://depth-anything-v2.github.io (fetched 2026-08-29T08:25:49.407770+00:00, sha 9b431dc87dd6)
- Data as of 2026-08-30T08:39:29.467469+00:00.