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CNN Output Size Formula Explained | How to Calculate Feature Map Dimensions explained in 60 Seconds
CNN Output Size Formula Explained | How to Calculate Feature Map Dimensions explained in 60 Seconds
In this YouTube Short, I explain the formula for calculating the output feature map size in a Convolutional Neural Network.
You’ll learn how filter size, padding, and stride affect the final output dimensions using the standard CNN formula:
Output Size
=
(
𝑊
−
𝐹
+
2
𝑃
)
𝑆
+
1
Output Size=
S
(W−F+2P)
+1
Perfect for beginners studying CNN maths, deep learning, and computer vision.
cnn output size, cnn formula, feature map size formula, convolution output calculation, deep learning basics, cnn maths, computer vision tutorial, stride padding filter formula, cnn layers, machine learning for beginners, deep learning short, ai education short, tech shorts
Видео CNN Output Size Formula Explained | How to Calculate Feature Map Dimensions explained in 60 Seconds канала Muhammad Usama Anwar
In this YouTube Short, I explain the formula for calculating the output feature map size in a Convolutional Neural Network.
You’ll learn how filter size, padding, and stride affect the final output dimensions using the standard CNN formula:
Output Size
=
(
𝑊
−
𝐹
+
2
𝑃
)
𝑆
+
1
Output Size=
S
(W−F+2P)
+1
Perfect for beginners studying CNN maths, deep learning, and computer vision.
cnn output size, cnn formula, feature map size formula, convolution output calculation, deep learning basics, cnn maths, computer vision tutorial, stride padding filter formula, cnn layers, machine learning for beginners, deep learning short, ai education short, tech shorts
Видео CNN Output Size Formula Explained | How to Calculate Feature Map Dimensions explained in 60 Seconds канала Muhammad Usama Anwar
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Информация о видео
10 декабря 2025 г. 17:14:04
00:01:11
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