Grounded SAM 2 Base Model¶
This repository contains the code implementing Grounded SAM 2 using Florence-2 as a grounding model and Segment Anything 2 as a segmentation model for use with autodistill.
Florence-2 is a zero-shot multimodal model. You can use Florence-2 for open vocabulary object detection. This project uses the object detection capabilities in Florence-2 to ground the SAM 2 model.
Installation¶
To use the Grounded SAM 2 base model, you will need to install the following dependency:
pip3 install autodistill-grounded-sam-2
Quickstart¶
from autodistill_grounded_sam_2 import GroundedSAM2
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2
# define an ontology to map class names to our Grounded SAM 2 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 = GroundedSAM2(
ontology=CaptionOntology(
{
"person": "person",
"shipping container": "shipping container",
}
)
)
# 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 all images in a folder called `context_images`
base_model.label("./context_images", extension=".jpeg")
License¶
The code in this repository is licensed under an Apache 2.0 license.
🏆 Contributing¶
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!