What is EvaCLIP?¶
EdgeSAM, introduced in the "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM" paper, is a faster version of the Segment Anything model.
Grounded EdgeSAM combines Grounding DINO and EdgeSAM, allowing you to identify objects and generate segmentation masks for them.
Installation¶
To use Grounded EdgeSAM with autodistill, you need to install the following dependency:
pip3 install autodistill-grounded-edgesam
Quickstart¶
from autodistill_clip import CLIP
# define an ontology to map class names to our GroundingDINO 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
from autodistill_grounded_edgesam import GroundedEdgeSAM
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2
# define an ontology to map class names to our GroundedSAM 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 = GroundedEdgeSAM(
ontology=CaptionOntology(
{
"person": "person",
"forklift": "forklift",
}
)
)
# run inference on a single image
results = base_model.predict("logistics.jpeg")
plot(
image=cv2.imread("logistics.jpeg"),
classes=base_model.ontology.classes(),
detections=results
)
# label a folder of images
base_model.label("./context_images", extension=".jpeg")
License¶
This repository is released under an S-Lab License 1.0 license.