Image ATM is a one-click tool that automates the workflow of a typical image classification pipeline in an opinionated way, this includes:

• Preprocessing and validating input images and labels
• Starting/terminating cloud instance with GPU support
• Training
• Model evaluation

Read the documentation at: https://idealo.github.io/imageatm/

Image ATM is compatible with Python 3.6 and is distributed under the Apache 2.0 license.

There are two ways to install Image ATM:

• Install Image ATM from PyPI (recommended):

pip install imageatm

• Install Image ATM from the GitHub source:

git clone https://github.com/idealo/imageatm.git
cd imageatm
python setup.py install

Run this in your terminal

imageatm pipeline config/config_file.yml

Run the data preparation:

from imageatm.components import DataPrep

dp = DataPrep(
    image_dir = 'sample_dataset/',
    samples_file = 'sample_configfile.json',
    job_dir='sample_jobdir/'
)
dp.run(resize=True)

Run the training:

from imageatm.components import Training

trainer = Training(dp.image_dir, dp.job_dir)
trainer.run()

Run the evaluation:

from imageatm.components import Evaluation

evaluater = Evaluation(image_dir=dp.image_dir, job_dir=dp.job_dir)
evaluater.run()

Please cite Image ATM in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{idealods2019imageatm,
  title={Image ATM},
  author={Christopher Lennan and Malgorzata Adamczyk and Gunar Maiwald and Dat Tran},
  year={2019},
  howpublished={\url{https://github.com/idealo/imageatm}},
}

• Christopher Lennan, github: clennan
• Malgorzata Adamczyk, github: gosia-malgosia
• Gunar Maiwald: github: gunarmaiwald
• Dat Tran, github: datitran

See LICENSE for details.

• We are currently using Keras 2.2. The plan is to use tf.keras once TF 2.0 is out. Currently tf.keras is buggy, especially with model saving/loading (tensorflow/tensorflow#22697)