From 267274d8000866be98d9c4607962bf66dd189aa8 Mon Sep 17 00:00:00 2001 From: pinb Date: Wed, 22 Nov 2023 12:19:56 +0900 Subject: [PATCH] update --- README.md | 65 ++++++++++++++++++++++++++++++++++++- images/dataset.png | Bin 0 -> 1396441 bytes images/googlenet.png | Bin 0 -> 326611 bytes images/inceptionnetv2.png | Bin 0 -> 240009 bytes images/inceptionnetv3.png | Bin 0 -> 262744 bytes images/inceptionnetv4.jpeg | Bin 0 -> 366728 bytes 6 files changed, 64 insertions(+), 1 deletion(-) create mode 100644 images/dataset.png create mode 100644 images/googlenet.png create mode 100644 images/inceptionnetv2.png create mode 100644 images/inceptionnetv3.png create mode 100644 images/inceptionnetv4.jpeg diff --git a/README.md b/README.md index cc12535..cb6835a 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,70 @@ subtitle: Deep Learning --- +# **InceptionNet이란?** +이미지 인식 및 분류 작업을 위해 Google에서 개발된 딥러닝 아키텍처이다. +InceptionNet의 여러 버전 중 가장 널리 알려진 것은 Inception v1 (GoogLeNet), Inception v2, Inception v3, Inception v4 등이 있다. + + +# **Inception v1 (GoogLeNet)** + +![inceptionnet](./images/googlenet.png) + +* 개발 배경 + +: 기존의 깊은 신경망이 컴퓨팅 자원과 과적합 문제를 겪는 것에 대한 해결책으로 개발되었다. + +* 특징 + +: 'Inception 모듈'이라고 불리는 빌딩 블록을 사용한다. + +이 모듈은 서로 다른 크기의 컨볼루션 필터들(1x1, 3x3, 5x5)과 맥스 풀링을 병렬적으로 적용하고 결과를 결합한다. + +이렇게 함으로써 다양한 스케일의 특징을 효과적으로 학습할 수 있다. + +* 성과 + +: 2014년 ILSVRC (ImageNet Large Scale Visual Recognition Challenge)에서 우승했다. + + +# **Inception v2** + +![inceptionnet](./images/inceptionnetv2.png) + +* 개선 사항 + +: Inception v1의 아이디어를 확장하여, 2D 컨볼루션 연산을 더 효율적으로 수행하는 방법이 제안되었다. + +큰 컨볼루션 필터(예: 5x5)를 여러 개의 작은 필터(예: 3x3)로 분해하여 연산 효율성을 개선했다. + + +# **Inception v3** + +![inceptionnet](./images/inceptionnetv3.png) + +* 개선 사항 + +: 더욱 정교한 Inception 모듈을 사용하고, 학습과정을 안정화하는 방법을 도입했다. + +5x5 컨볼루션을 두 개의 3x3 컨볼루션으로 대체하고, RMSProp 옵티마이저, 라벨 스무딩, 배치 정규화 등을 사용하여 성능을 향상시켰다. + + +# **Inception v4** + +![inceptionnet](./images/inceptionnetv4.jpeg) + +* 개발 배경 + +: Inception 아키텍처와 ResNet 아키텍처의 아이디어를 결합했다. + +* 특징 + +: Inception-ResNet이라고도 불리며, Inception 모듈에 잔차 연결(Residual Connections)을 추가하여 네트워크를 더 깊게 만들면서도 학습을 안정화하였다. + +InceptionNet은 다양한 크기의 컨볼루션 필터와 풀링 레이어를 병렬로 적용함으로써 이미지의 다양한 특징을 효과적으로 추출할 수 있으며, + +네트워크가 깊어짐에 따라 발생할 수 있는 과적합 문제와 학습의 어려움을 극복하기 위한 여러 기술을 적용한다. --- @@ -809,4 +872,4 @@ plt.show() - GAN - Google - ChatGPT -- [파이토치 GAN 튜토리얼](https://tutorials.pytorch.kr/beginner/dcgan_faces_tutorial.html) +- [GitHub](https://github.com/Seonghoon-Yu/AI_Paper_Review/blob/master/Classification/Inceptionv4(2016).ipynb) diff --git a/images/dataset.png b/images/dataset.png new file mode 100644 index 0000000000000000000000000000000000000000..1bacde2ffb5699876c4ae6ad88bc92e8e04ab0bf GIT binary patch literal 1396441 zcmeFad9Tb2vYDEHpED3~|ghCW-aL5EkSvUa^gA$Wv zR!lGsA!Iqri9=Qv0kZ%*F}AZXXaN=iOF|4{P)n^=tEF!B`1+0a-SNz0e!pGcIrl!v zApM(;zW44q^;PZKwQJX|y{mQ&=cm8@UEjFYJm1W+Z0(!B>Fw{%vf@AE?^o+ZVt)1$ z-~LzlYW$bq^w)oc^w0QL_`3Uk?njBd^L^j&UGMw)AAZmGyzg)R(0gzHj=%YX@5vs1 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