X - F + 2P S

@ejlbell

1 +

Convolution

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ImageConvoluted

Feature

Image Filters

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+1

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X

X

X - F + 2P S

X = image size

1 +

X

X

F

X = image size

F = filter size

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

F

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

X

XX = image size

F = filter size

P = padding

S = stride

P

X - F + 2P S

1 +

Filter Size

1 × 1 3 × 3 7 × 7 9 × 95 × 5

Max Pooling

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8

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3X

Y

Example: VGG

19 layers

3x3 convolution

pad 1

stride 1

19 layers

3x3 convolution

pad 1

stride 1

X - F + 2P S

1 + = X

Example: VGG

Resources • image size

• parameters (dense vs conv)

• parallelisation (data vs model)

thanks @ejlbell

References