Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/autodistill/autodistill-dinov2

DINOv2 module for use with Autodistill.

https://github.com/autodistill/autodistill-dinov2

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.2%) to scientific vocabulary

Keywords

autodistill computer-vision dinov2
Last synced: 11 months ago · JSON representation

Repository

DINOv2 module for use with Autodistill.

Basic Info
Statistics
  • Stars: 14
  • Watchers: 2
  • Forks: 3
  • Open Issues: 1
  • Releases: 0
Topics
autodistill computer-vision dinov2
Created about 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Autodistill DINOv2 Module

This repository contains the code supporting the DINOv2 base model for use with Autodistill.

DINOv2, developed by Meta Research, is a self-supervised training method for computer vision models. This library uses DINOv2 image embeddings with SVM to build a classification model.

Read the full Autodistill documentation.

Read the DINOv2 Autodistill documentation.

Installation

To use DINOv2 with autodistill, you need to install the following dependency:

bash pip3 install autodistill-dinov2

Quickstart

```python from autodistill_dinov2 import DINOv2

target_model = DINOv2(None)

train a model

specify the directory where your annotations (in multiclass classification folder format)

DINOv2 embeddings are saved in a file called "embeddings.json" the folder in which you are working

with the structure {filename: embedding}

targetmodel.train("./contextimages_labeled")

get class list

print(target_model.ontology.classes())

run inference on the new model

pred = targetmodel.predict("./contextimages_labeled/train/images/dog-7.jpg")

print(pred) ```

License

The code in this repository is licensed under a CC Attribution-NonCommercial 4.0 International license.

🏆 Contributing

We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!

Owner

  • Name: Autodistill
  • Login: autodistill
  • Kind: organization
  • Email: autodistill@roboflow.com

Use bigger slower models to train smaller faster ones

GitHub Events

Total
  • Watch event: 4
Last Year
  • Watch event: 4

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 1
  • Average time to close issues: 2 months
  • Average time to close pull requests: 24 days
  • Total issue authors: 3
  • Total pull request authors: 1
  • Average comments per issue: 1.33
  • Average comments per pull request: 1.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kobic8 (1)
  • ccmCCMfk (1)
  • rainbowpuffpuff (1)
Pull Request Authors
  • computerscienceiscool (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 41 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
pypi.org: autodistill-dinov2

DINOv2 module for use with Autodistill

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 41 Last month
Rankings
Dependent packages count: 7.2%
Forks count: 23.0%
Average: 24.9%
Stargazers count: 28.2%
Dependent repos count: 41.3%
Maintainers (1)
yeldarb
Last synced: 11 months ago

Dependencies

.github/workflows/publish-to-pypi.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • pypa/gh-action-pypi-publish release/v1 composite
.github/workflows/publish.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v2 composite
.github/workflows/test.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v2 composite
.github/workflows/welcome.yml actions
  • actions/first-interaction v1.1.1 composite
setup.py pypi
  • Pillow *
  • autodistill *
  • numpy *
  • scikit-learn *
  • supervision *
  • torch *
  • torchvision *
  • tqdm *