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

Use PaliGemma to auto-label data for use in training fine-tuned vision models.

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

Science Score: 13.0%

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    Low similarity (12.6%) to scientific vocabulary

Keywords

autodistill computer-vision fine-tuning-computer-vision paligemma zero-shot-object-detection
Last synced: 9 months ago · JSON representation

Repository

Use PaliGemma to auto-label data for use in training fine-tuned vision models.

Basic Info
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  • Stars: 12
  • Watchers: 3
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Topics
autodistill computer-vision fine-tuning-computer-vision paligemma zero-shot-object-detection
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme

README.md

Autodistill PaLiGemma Module

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

PaLiGemma, developed by Google, is a computer vision model trained using pairs of images and text. You can label data with PaliGemma models for use in training smaller, fine-tuned models with Autodisitll.

Read the full Autodistill documentation.

Installation

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

bash pip3 install autodistill-paligemma

Quickstart

Auto-label with an existing model

```python from autodistill_paligemma import PaliGemma

define an ontology to map class names to our PaliGemma 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 = PaliGemma( ontology=CaptionOntology( { "person": "person", "a forklift": "forklift" } ) )

label a single image

result = PaliGemma.predict("test.jpeg") print(result)

label a folder of images

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

Model fine-tuning

You can fine-tune PaliGemma models with LoRA for deployment with Roboflow Inference.

To train a model, use this code:

```python from autodistill_paligemma import PaLiGemmaTrainer

target_model = PaLiGemmaTrainer()

train a model

target_model.train("./data/") ```

License

The model weights for PaLiGemma are licensed under a custom Google license. To learn more, refer to the Google Gemma Terms of Use.

🏆 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

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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 36 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: autodistill-paligemma

Auto-label data with a PaliGemma model, or ine-tune a PaLiGemma model using custom data with Autodistill.

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 36 Last month
Rankings
Dependent packages count: 10.9%
Average: 36.2%
Dependent repos count: 61.5%
Maintainers (1)
capjamesg
Last synced: 9 months ago

Dependencies

setup.py pypi
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