This repository contains the code supporting the FastViT base model for use with Autodistill.
FastViT, developed by Apple, is a classification model that supports zero-shot classification.
Read the full Autodistill documentation.
Read the FastViT Autodistill documentation.
To use FastViT with autodistill, you need to install the following dependency:
pip3 install autodistill-fastvit
FastViT works using the ImageNet-1k class list. This class list is available in the FASTVIT_IMAGENET_1K_CLASSES
variable.
You can provide classes from the list to retrieve predictions for a specific class in the list. You can also provide a custom ontology to map classes from the list to your own classes.
from autodistill_fastvit import FastViT, FASTVIT_IMAGENET_1K_CLASSES
from autodistill.detection import CaptionOntology
# zero shot with no prompts
base_model = FastViT(None)
# zero shot with prompts from FASTVIT_IMAGENET_1K_CLASSES
base_model = FastViT(
ontology=CaptionOntology(
{
"coffeemaker": "coffeemaker",
"ice cream": "ice cream"
}
)
)
predictions = base_model.predict("./example.png")
labels = [FASTVIT_IMAGENET_1K_CLASSES[i] for i in predictions.class_id.tolist()]
print(labels)
See LICENSE for the model license.
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