Awesome

BoB-OOD-Classification

This repository is the official implementation of <strong>Out-of-Distribution Image Classification</strong> task in the Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks.

Dependencies

Version Control of Python libraries in environment.yml file. To create a virtual environment:

conda env create -f environment.yml

Classification OOD experiments

Dependencies:

Install tlllib

python3 -m pip install -i https://test.pypi.org/simple/ tllib==0.4

Instructions

We include shell scripts to benchmark OOD generalization performance for image classification: for robustness to style and structure variations on ImageNet variants (ImageNet Sketch, Renditions, Adversarial, and V2), and synthetic-to-real generalization on VisDA2017.

To evaluate on ImageNet variants, run:

bash eval_classifier_ood.sh

To train on VisDA (syn), and evaluate on VisDA (real), run:

bash eval_classifier_ood.sh

Update the following variables as appropriate:

BACKBONE, VALID_LABELS_INA, VALID_LABELS_INR, CHECKPOINT, DATA_DIR