This repository contains the code supporting the EfficientSAM base model for use with Autodistill.
EfficientSAM is an image segmentation model that was introduced in the paper "EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything". You can use EfficientSAM with autodistill for image segmentation.
Read the full Autodistill documentation.
To use EfficientSAM with Autodistill, you need to install the following dependency:
pip3 install autodistill-efficientsamThis model returns segmentation masks for all objects in an image.
If you want segmentation masks only for specific objects matching a text prompt, we recommend combining EfficientSAM with a zero-shot detection model like GroundingDINO.
Read our ComposedDetectionModel documentation for more information about how to combine models like EfficientSAM and GroundingDINO.
from autodistill_efficientsam import EfficientSAM
# define an ontology to map class names to our EfficientSAM 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 = EfficientSAM(None)
masks = base_model.predict("./image.png")This project is licensed under an Apache 2.0 license.
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