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:clipboard:This is a miniature of ImageNet classification Challenge.

Python 99.18% Shell 0.82%
tiny-imagenet200 se-resnext

tiny_imagenet_challenge's Introduction

Tiny_ImageNet_Challenge

Tiny ImageNet Challenge: This is a miniature of ImageNet classification Challenge.

  • Docker >= 19.03.8
  • CUDA >= 10.2

Getting Started

Download git and dataset

git clone https://github.com/cjf8899/Tiny_ImageNet_Challenge.git

cd Tiny_ImageNet_Challenge
cd data
sh download_and_unzip.sh

# Pretrain model download(ImageNet)
wget http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth

the structures would like

~/Tiny_ImageNet_Challenge/data/
    -- tiny-imagenet-200
    -- se_resnext50_32x4d-a260b3a4.pth

Train

I used wandb and various other transforms as well.

The wandb code is included in this repositories.

Wandb Guide

All of the special transforms I used are included in this repositories.

Results

I used SE-ResNeXt50_32x4d and the best performance is 82.54%

Implementation top-1 top-5
SE-ResNeXt50 82.54 94.96

memoryblock

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