Skip to main content

FastSAM module for use with Autodistill

Project description

Autodistill FastSAM Module

This repository contains the code supporting the FastSAM base model for use with Autodistill.

FastSAM is a segmentation model trained on 2% of the SA-1B dataset used to train the Segment Anything Model.

Read the full Autodistill documentation.

Read the FastSAM Autodistill documentation.

Installation

To use FastSAM with autodistill, you need to install the following dependency:

pip3 install autodistill-fastsam

Quickstart

[!NOTE]

When you first run this model, the installation process will start. Inference may take a few seconds (in testing, up to 30 seconds) while the model is downloaded and installed. Once the model is installed, inference will be much faster.

from autodistill_fastsam import FastSAM

# define an ontology to map class names to our FastSAM 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 = FastSAM(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")

License

This project 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!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autodistill-fastsam-0.1.1.tar.gz (8.2 kB view details)

Uploaded Source

Built Distribution

autodistill_fastsam-0.1.1-py3-none-any.whl (8.9 kB view details)

Uploaded Python 3

File details

Details for the file autodistill-fastsam-0.1.1.tar.gz.

File metadata

  • Download URL: autodistill-fastsam-0.1.1.tar.gz
  • Upload date:
  • Size: 8.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for autodistill-fastsam-0.1.1.tar.gz
Algorithm Hash digest
SHA256 87aad8b2ae9f6ec785bf43a21c5ec651e3eb483fca30692733f6eb97a85e7c39
MD5 203237de037c541caae25da2fe316797
BLAKE2b-256 daa8ef8366ea661d09a938428b74d7a878a62520b5956b827b2191a66c1cbbcc

See more details on using hashes here.

File details

Details for the file autodistill_fastsam-0.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for autodistill_fastsam-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 633c2f28390095baa91c3aeed7f69ad17cb38896c6831f9da9ad96086901fa4c
MD5 2896faf2d9de936c94924ef1778f5543
BLAKE2b-256 f5616d32300564997ac95de1b30246e80737a20a66e58f688d7baf95cb6b5466

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page