The iccv2023-ood-cv-challenge-classification-track-self-supervised-pretrain-3rd-place-solution from itsnotacie

iccv2023-ood-cv-challenge-classification-track-self-supervised-pretrain-3rd-place-solution's Introduction

Requirement

Create a conda virtual environment and activate it.

conda create --name ood2023 python=3.8.13
conda activate ood2023

Install Pytorch and torchvision following the official instructions, e.g.,

conda install pytorch==1.13.1 torchvision==0.14.1 -c pytorch

Install the dependent libraries.

pip install -r requirements.txt

Data Preparation

Download the data set from the official website and organize it into the following data structure.

dataset/OODCV2023
    train
	Images
            aeroplane/
	    ...
            train/
    	labels.csv
    phase1-test-images
    phase2-test-images

Getting Started

train

We use one Nvidia Tesla 4090 (24G) GPU for training. You need to modify the params in the configs/beitv2.yaml file or configs/eva02.yaml before training, like: data model or pretrained weight path in pretrained_cfg. Run different bash files for different models.

./train_eva02.sh 0 or ./train_beitv2.sh 0

Test

You need to modify the checkpoint_list in the eval_tta.py file before testing.

python eval_tta.py

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