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.
To use PaLiGemma with autodistill, you need to install the following dependency:
pip3 install autodistill-paligemmafrom 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
base_model.label("./context_images", extension=".jpeg")You can fine-tune PaliGemma models with LoRA for deployment with Roboflow Inference.
To train a model, use this code:
from autodistill_paligemma import PaLiGemmaTrainer
target_model = PaLiGemmaTrainer()
# train a model
target_model.train("./data/")The model weights for PaLiGemma are licensed under a custom Google license. To learn more, refer to the Google Gemma Terms of Use.
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!