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Video Depth Anything Small

This repository contains the model described in Video Depth Anything: Consistent Depth Estimation for Super-Long Videos.

ArtOtherVersionRevision 2568753BBewertung

Modellquelle

Art
Other
Dateigröße
111 MB
Version
vRevision 2568753
Aktualisiert
1. Nov. 2026
Downloads
0
Quelle
Hugging Face

Quellenbeschreibung

Video Depth Anything

This repository contains the model described in Video Depth Anything: Consistent Depth Estimation for Super-Long Videos.

Project Page: https://videodepthanything.github.io

About

This model is based on Depth Anything V2, and can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy.

Usage

git clone https://github.com/DepthAnything/Video-Depth-Anything
cd Video-Depth-Anything
pip install -r requirements.txt

Download the checkpoints listed here and put them under the checkpoints directory.

bash get_weights.sh

Inference a video

python3 run.py --input_video ./assets/example_videos/davis_rollercoaster.mp4 --output_dir ./outputs --encoder vitl

Quellen

1 Quelle
Hugging FacePrimärVersion 256875362cff76724b920335dfb4b29dd611f66e
huggingface.co/depth-anything/Video-Depth-Anything-Small/tree/256875362cff76724b920335dfb4b29dd611f66e

Verifiziert 1. Nov.

Modellartefakte

1 Artefakt

video_depth_anything_vits.pth

pth · 111 MB · SHA-256 13379300b739…3609 · Hugging Face

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Quellenauszüge

2 Auszüge

--- license: apache-2.0 library_name: pytorch pipeline_tag: depth-estimation --- # Video Depth Anything This repository contains the model described in [Video Depth Anything: Consistent Depth Estimation for Super-Long Videos](https://huggingface.co/papers/2501.12375). Project Page: https://videodepthanything.github.io ## About This model is based on [Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2), and can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy. ## Usage ```bash git clone https://github.com/DepthAnything/Video-Depth-Anything cd Video-Depth-Anything pip install -r requirements.txt ``` Download the checkpoints listed [here](#pre-trained-models) and put them under the `checkpoints` directory. ```bash bash get_weights.sh ``` ### Inference a video ```bash python3 run.py --input_video ./assets/example_videos/davis_rollercoaster.mp4 --output_dir ./outputs --encoder vitl ```

Source context: 0 downloads · 11 likes · Pipeline depth-estimation · Library pytorch · Repo depth-anything/Video-Depth-Anything-Small