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Easy-ICD: Automatic scraping and denoising of image classification datasets

CMPSC 445 Final Project

Installation

  1. Make sure you have the latest versions of Git and Python downloaded beforehand.
  2. Open the command line interface in the directory you want to store Easy-ICD.
  3. Enter and run the following command lines below:
git clone https://github.com/SashwatAnagolum/Easy-ICD.git
cd Easy-ICD/easy_icd
pip install --editable .
  1. Additional pip installs such as Jupyter Notebook may be required if not already installed.

Using Easy-ICD

See the examples folder for Jupyter Notebooks illustrating how to use Easy-ICD for scraping images, training an outlier detector, and using a trained model to identify and mark images for removal from a scraped dataset.

To reproduce all the figures for CIFAR-10-Alt, Cars & Deer, and Cats & Dogs datasets from scratch, use the Jupyter Notebooks in the pretrained_models folder. To save time by using pretrained models, load the state_dicts of the models saved in the pretrained_models/models folder intead of training from scratch. Instructions: https://pytorch.org/tutorials/beginner/saving_loading_models.html