This repository contains the code supporting the FastSAM base model for use with Autodistill.
FastSAM is a segmentation model trained on 2% of the SA-1B dataset used to train the Segment Anything Model.
Read the full Autodistill documentation.
Read the FastSAM Autodistill documentation.
To use FastSAM with autodistill, you need to install the following dependency:
pip3 install autodistill-fastsam[!NOTE]
When you first run this model, the installation process will start. Inference may take a few seconds (in testing, up to 30 seconds) while the model is downloaded and installed. Once the model is installed, inference will be much faster.
from autodistill_fastsam import FastSAM
# define an ontology to map class names to our FastSAM 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 = FastSAM(
ontology=CaptionOntology(
{
"person": "person",
"a forklift": "forklift"
}
)
)
base_model.label("./context_images", extension=".jpeg")This project is licensed under an Apache 2.0 license.
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!