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Automated & Augmented ML Toolbox for Image Classification

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AnaMiguelRodrigues1/autolens

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autolens

Purpose

Effortlessly implementation of pre-existing code from open-source Augmented ML and Automated ML tools that support image classification tasks

Usage

This project can be both used as a library and CLI tool.

Dataset preparation

  • Main folder with sub-folders, each named with an integer number

Library Setup

  • clone repo: git clone https://github.com/AnaMiguelRodrigues1/autolens.git
  • move to the root of the project
  • install the library: python3.9 setup.py install
  • install python virtual environment on the root folder: python -m venv {automl_tool}_venv
  • run source -m {automl_tool}_venv/bin/activate
from autolens.LUDWIG.run import main #prior selection of automl tool

main(
    "../../chest_xray/", #dataset path 
    1, #bigger steps for less computational resources
    (255, 255), #target size
    0.2, #size of testing dataset
    0.1 #size of validation dataset
)

CLI Interaction

  • clone repo: git clone https://github.com/AnaMiguelRodrigues1/autolens.git
  • move to the root of the project
python3.9 autolens.py "ludwig" "../../chest_xray"
  --target_size "(255, 255)"
  --test_percentage "0.2"
  --val_percentage "0.1"
  --clean_metadata "store_true"
  --cache_dir "{home_dir}/.cache/autolens"

Configuration Details

S.F. - Supported Framework I.S. - Interface Solutions Lang. - Programming Language O.S. - Operative System

Fastai v2.7.12 Ktrain v0.37.2 Ludwig v0.8.1.post1 Autogluon1 v0.8.2 Autokeras v1.1.0
S.F. Pytorch v1.13.1 Tensorflow v2.11 Tensorflow2 Pytorch v1.13.1 Tensorflow v>=2.8.03
I.S. API API API/CLI API API
Lang. Python v3.7-v3.10 Python v3.6-v3.10 Python v>=3.8 Python v3.8-v3.10 Python v3.8-v3.11
O.S. Linux, Windows Linux Linux, Windows Linux, Windows4 Linux, Windows5, MacOS

More Information

Footnotes

  1. Uses Fastai as one of the installation requirements.

  2. Uses Tensorboard v2.14, a visualization toolkit from Tensorflow.

  3. Tensorflow v2.9.1 most compatible with the remaining dependencies.

  4. Advisable to use Anaconda.

  5. Requires Microsoft Visual C++ and v>7.