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FCTL-code

Introduction

The code will be sorted and published soon.

Getting started

1. Installation

FCTL is developed based on torch==1.13.0 torchvision==0.14.0 pytorch-lightning==2.2.1

2. Preparing Data

We provide the detail about the benchmark datasets in data/.

Evaluation

Dataset Backbone AUROC Log Weight (Backup)
MNIST ResNet101 0.999 🤗split1 split2 split3 split4 split5
SVHN ResNet101 0.972
CIFAR10 ResNet101 0.948
CIFAR+10 ResNet101 0.990
CIFAR+50 ResNet101 0.979
TinyImageNet ResNet101 0.951
CUB ResNet101 0.812 split1_log
Aircraft ResNet101 0.853 split1_log 🤗split1
MNIST ViT/B16 0.983 split1_log
SVHN ViT/B16 0.974
CIFAR10 ViT/B16 0.987 🤗split1 split2 split3 split4 split5
CIFAR+10 ViT/B16 0.995 🤗split1 split2 split3 split4 split5
CIFAR+50 ViT/B16 0.991 🤗split1 split2 split3 split4 split5
TinyImageNet ViT/B16 0.978 🤗split1 split2 split3 split4 split5
CUB ViT/B16 0.895 🤗split1
Aircraft ViT/B16 0.817 split1_log
CIFAR10-ood ResNet101 🤗split1

https://drive.google.com/file/d/12apkcoi-YQRP5IK9MVBQ_r-zFw6byLyE/view?usp=drive_link

chmod +x _scripts/*/test*.sh

Training

The code will be sorted and published soon.

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