https://github.com/autodistill/autodistill-grounded-edgesam
EdgeSAM model for use with Autodistill.
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Low similarity (12.1%) to scientific vocabulary
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Repository
EdgeSAM model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://docs.autodistill.com
- Size: 13.7 KB
Statistics
- Stars: 26
- Watchers: 3
- Forks: 2
- Open Issues: 1
- Releases: 0
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Metadata Files
README.md
Autodistill Grounded EdgeSAM Module
This repository contains the code supporting the Grounded EdgeSAM base model for use with Autodistill.
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.
Read the full Autodistill documentation to learn more about Autodistill.
Installation
To use Grounded EdgeSAM with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-grounded-edgesam
Quickstart
```python 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 autodistillgroundededgesam 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
basemodel.label("./contextimages", extension=".jpeg") ```
License
This repository is released under an S-Lab License 1.0 license.
🏆 Contributing
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!
Owner
- Name: Autodistill
- Login: autodistill
- Kind: organization
- Email: autodistill@roboflow.com
- Website: https://autodistill.com
- Repositories: 1
- Profile: https://github.com/autodistill
Use bigger slower models to train smaller faster ones
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Dependencies
- autodistill *
- numpy >=1.20.0
- opencv-python >=4.6.0
- rf_groundingdino *
- rf_segment_anything *
- supervision *
- torch *