Table 2 Structure of ResNet 18

From: A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification

Input

Parameters

Feature map

Output size

Kernel size

Stride

1

Conv1

64

112 × 112 × 64

7 × 7, 64

3 × 3 max pooling

2

2

Conv2_x

64

56 × 56 × 64

\(\:\left[\begin{array}{c}3\times\:3,\:64\\\:3\times\:3,64\end{array}\right]\times\:2\)

2

6

Conv3_x

128

28 × 28 × 128

\(\:\left[\begin{array}{c}3\times\:3,\:128\\\:3\times\:3,128\end{array}\right]\times\:2\)

2

10

Conv4_x

256

14 × 14 × 256

\(\:\left[\begin{array}{c}3\times\:3,\:256\\\:3\times\:3,256\end{array}\right]\times\:2\)

2

14

Conv5_x

512

7 × 7 × 512

\(\:\left[\begin{array}{c}3\times\:3,\:512\\\:3\times\:3,512\end{array}\right]\times\:2\)

2

18

Avg. pool

--

1 × 1 × 512

7 × 7 average pool

--

19

FC

--

1000

--

--

20

Output

--

1000

--

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