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https://github.com/autodistill/autodistill-owlv2

OWLv2 base model for use with Autodistill.

https://github.com/autodistill/autodistill-owlv2

Science Score: 13.0%

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    Low similarity (12.0%) to scientific vocabulary

Keywords

autodistill computer-vision object-detection owlv2 zero-shot-object-detection
Last synced: 5 months ago · JSON representation

Repository

OWLv2 base model for use with Autodistill.

Basic Info
  • Host: GitHub
  • Owner: autodistill
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage: https://docs.autodistill.com
  • Size: 10.7 KB
Statistics
  • Stars: 5
  • Watchers: 3
  • Forks: 6
  • Open Issues: 6
  • Releases: 0
Topics
autodistill computer-vision object-detection owlv2 zero-shot-object-detection
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

Autodistill OWLv2 Module

This repository contains the code supporting the OWLv2 base model for use with Autodistill.

OWLv2 is a zero-shot object detection model that follows from on the OWL-ViT architecture. OWLv2 has an open vocabulary, which means you can provide arbitrary text prompts for the model. You can use OWLv2 with autodistill for object detection.

Read the full Autodistill documentation.

Read the OWLv2 Autodistill documentation.

Installation

To use OWLv2 with autodistill, you need to install the following dependency:

bash pip3 install autodistill-owlv2

Quickstart

```python from autodistill_owlv2 import OWLv2 from autodistill.detection import CaptionOntology from autodistill.utils import plot import cv2

define an ontology to map class names to our OWLv2 prompt

the ontology dictionary has the format {caption: class}

where caption is the prompt sent to the base model, and class is the label that will

be saved for that caption in the generated annotations

then, load the model

base_model = OWLv2( ontology=CaptionOntology( { "person": "person", "dog": "dog" } ) )

run inference on a single image

results = base_model.predict("dog.jpeg")

plot( image=cv2.imread("dog.jpeg"), classes=base_model.ontology.classes(), detections=results )

label a folder of images

basemodel.label("./contextimages", extension=".jpeg") ```

License

This model is licensed under an Apache 2.0 (see original model implementation license, and the corresponding HuggingFace Transformers documentation).

🏆 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

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  • Issues event: 1
  • Pull request event: 2
  • Fork event: 3
Last Year
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Last synced: 9 months ago

All Time
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  • Total Committers: 1
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.0
Past Year
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Committer Domains (Top 20 + Academic)
jamesg.blog: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 3
  • Total pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: over 1 year
  • Total issue authors: 3
  • Total pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
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  • R3xpook (1)
  • taherpat (1)
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  • R3xpook (2)
  • jakubhejhal (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 204 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: autodistill-owlv2

OWLv2 base model for use with Autodistill.

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 204 Last month
Rankings
Dependent packages count: 9.4%
Average: 38.7%
Dependent repos count: 68.1%
Maintainers (1)
yeldarb
Last synced: 6 months ago