This document discusses very deep convolutional networks for large-scale image recognition. It describes network configurations that use 3x3 convolutional filters with max pooling layers and fully connected layers. The networks have 11 or 19 weight layers and use 1x1 convolutional filters to introduce nonlinearity. Classification experiments on ImageNet data with over 1 million training images achieve top-1 and top-5 error rates.

1. VERY DEEP CONVOLUTION NETWORKS
FOR LARGE-SCALE IMAGE RECOGNITION
Feb 21, 2021
HeeDae Kwon

2. Contents
• 1. INTRODUCTION
• 2. ConvNet Configurations
• 3. Classification Framwork
• 4. Classification experiments

3. INTRODUCTION
• 더 깊어진 CONV구조
• Use 3x3 conv filter
• Accuracy

4. ConvNet configuration
Architecture
Training time Input image 224x224
Use filter 3x3 , 1x1
max-pooling layer 5개 , 2x2 pixel window, stride2
FC(Fully connected) 3개 ((1,2 4096) ,(3 , 1000))
Activation function ReLU

5. configuration
• A 11 weight layer (8conv 3 fc)
E 19 weight layer(16conv 3fc)
• Maxpooling
64 =>512
• 적은 파라미터 개수
Why?
=> 3x3필터 3개 = 7x7 1개
n-7+1 = ((n-3+1)-3+1)-3+1
연산량은 3(3^2C^2) = 27C^2 ,
7^C^2 = 49C^2
1x1 conv
=> 비선형성을 부여하기 위한 용
도 1x1을 이용하면 ReLU거침

6. Classification Framwork
트레이닝단계
Batch size 256
momentum 0.9
weight decay 0.00005
epoch 74
Learning rate 0.01 => 10배 감소

7. Testing
• Rescaled test image
• Convert fully-connected layers to convloution layer
• Data augmentaion

8. Experiments
The top-1 error : 예측이 잘못된 이미지의 비율
The top-5 error : 예측된 범주에 정답이 없는 이미지의 비율
Dataset
Class 1000
Train image 1.3M
Validation image 50K
Test image 100K

9. Evaluation

10. Evaluation

11. Evaluation

12. •감사합니다.