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https://github.com/autodistill/autodistill-grounded-sam

GroundedSAM Base Model plugin for Autodistill

https://github.com/autodistill/autodistill-grounded-sam

Science Score: 13.0%

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    Low similarity (13.6%) to scientific vocabulary

Keywords

autodistill grounded-sam grounding-dino
Last synced: 9 months ago · JSON representation

Repository

GroundedSAM Base Model plugin for Autodistill

Basic Info
  • Host: GitHub
  • Owner: autodistill
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage: https://docs.autodistill.com
  • Size: 13.7 KB
Statistics
  • Stars: 51
  • Watchers: 3
  • Forks: 10
  • Open Issues: 6
  • Releases: 0
Topics
autodistill grounded-sam grounding-dino
Created about 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

Autodistill: GroundedSAM Base Model

This repository contains the code implementing GroundedSAM as a Base Model for use with autodistill.

GroundedSAM combines GroundingDINO with the Segment Anything Model to identify and segment objects in an image given text captions.

Read the full Autodistill documentation.

Read the GroundedSAM Autodistill documentation.

[!TIP] You can use Autodistill Grounded SAM on your own hardware using the instructions below, or use the Roboflow hosted version of Autodistill to label images in the cloud.

Installation

To use the GroundedSAM Base Model, simply install it along with a Target Model supporting the detection task:

bash pip3 install autodistill-grounded-sam autodistill-yolov8

You can find a full list of detection Target Models on the main autodistill repo.

Quickstart

```python from autodistillgroundedsam import GroundedSAM 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 = GroundedSAM( 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

basemodel.label("./contextimages", 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!

Owner

  • Name: Autodistill
  • Login: autodistill
  • Kind: organization
  • Email: autodistill@roboflow.com

Use bigger slower models to train smaller faster ones

GitHub Events

Total
  • Watch event: 7
  • Fork event: 2
Last Year
  • Watch event: 7
  • Fork event: 2

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 4
  • Total Committers: 2
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.25
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
James Gallagher j****g@j****g 3
Brad Dwyer b****d@r****m 1
Committer Domains (Top 20 + Academic)
roboflow.com: 1 jamesg.blog: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 12
  • Total pull requests: 2
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 4 months
  • Total issue authors: 10
  • Total pull request authors: 2
  • Average comments per issue: 2.25
  • Average comments per pull request: 1.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • Jordan-Pierce (2)
  • Siwakonrome (1)
  • nihirv (1)
  • JALALDATA (1)
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  • JudithR (1)
  • tfriedel (1)
  • pythonstuff8 (1)
  • csv610 (1)
  • shersoni610 (1)
Pull Request Authors
  • Youho99 (2)
  • ssun-g (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 2,735 last-month
  • Total dependent packages: 7
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: autodistill-grounded-sam

Automatically distill large foundational models into smaller, in-domain models for deployment

  • Versions: 6
  • Dependent Packages: 7
  • Dependent Repositories: 1
  • Downloads: 2,735 Last month
  • Docker Downloads: 0
Rankings
Stargazers count: 2.3%
Docker downloads count: 3.4%
Downloads: 5.3%
Forks count: 5.5%
Dependent packages count: 7.3%
Average: 7.6%
Dependent repos count: 22.1%
Maintainers (1)
yeldarb
Last synced: 10 months ago

Dependencies

.github/workflows/publish.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v2 composite
.github/workflows/test.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v2 composite
.github/workflows/welcome.yml actions
  • actions/first-interaction v1.1.1 composite
requirements.txt pypi
  • autodistill *
  • numpy >=1.20.0
  • opencv-python >=4.6.0
  • rf_groundingdino *
  • rf_segment_anything *
  • supervision *
  • torch *
setup.py pypi