Computer Vision and Deep
Learning
UNIT - III
Convolutional Neural Network
Notes
Introduction: Convolutional Neural Network (CNN)
A Convolutional Neural Network (CNN) is a type of Deep Learning architecture commonly used
for image classification and recognition tasks. It consists of multiple layers, including
Convolutional layers, Pooling layers, and fully connected layers. The Convolutional layer applies
filters to the input image to extract features, the Pooling layer downsamples the image to
reduce computation, and the fully connected layer makes the final prediction. The network
learns the optimal filters through backpropagation and gradient descent.
Artificial Neural Networks are used in various classification tasks like image, audio, words.
Different types of Neural Networks are used for different purposes, for example for predicting
the sequence of words we use Recurrent Neural Networks more precisely an LSTM, similarly for
image classification we use Convolution Neural networks.
Convolution Neural Network
Convolution Neural Networks or covnets are neural networks that share their parameters.
Imagine you have an image. It can be represented as a cuboid having its length, width
(dimension of the image), and height (as images generally have red, green, and blue channels).
,Now imagine taking a small patch of this image and running a small neural network on it, with
say, k outputs and represent them vertically. Now slide that neural network across the whole
image, as a result, we will get another image with different width, height, and depth. Instead of
just R, G, and B channels now we have more channels but lesser width and height. This
operation is called Convolution. If the patch size is the same as that of the image it will be a
regular neural network. Because of this small patch, we have fewer weights.
Now let’s talk about a bit of mathematics that is involved in the whole convolution process.
● Convolution layers consist of a set of learnable filters (a patch in the above image). Every
filter has small width and height and the same depth as that of input volume (3 if the
input layer is image input).
● For example, if we have to run convolution on an image with dimension 34x34x3. The
possible size of filters can be axax3, where ‘a’ can be 3, 5, 7, etc but small as compared to
image dimension.
● During forward pass, we slide each filter across the whole input volume step by step
where each step is called stride (which can have value 2 or 3 or even 4 for high
dimensional images) and compute the dot product between the weights of filters and
patch from input volume.
● As we slide our filters we’ll get a 2-D output for each filter and we’ll stack them together
and as a result, we’ll get output volume having a depth equal to the number of filters.
The network will learn all the filters.
●
Layers used to build ConvNets : A covnets is a sequence of layers, and every layer transforms
one volume to another through a differentiable function.
, Types of layers:
Let’s take an example by running a covnets on of image of dimension 32 x 32 x 3.
● Input Layer: This layer holds the raw input of the image with width 32, height 32, and
depth 3.
● Convolution Layer: This layer computes the output volume by computing the dot
product between all filters and image patches. Suppose we use a total of 12 filters for
this layer we’ll get output volume of dimension 32 x 32 x 12.
● Activation Function Layer: This layer will apply an element-wise activation function to
the output of the convolution layer. Some common activation functions are RELU:
max(0, x), Sigmoid: 1/(1+e^-x), Tanh, Leaky RELU, etc. The volume remains unchanged
hence output volume will have dimension 32 x 32 x 12.
● Pool Layer: This layer is periodically inserted in the covnets and its main function is to
reduce the size of volume which makes the computation fast reduces memory and also
prevents overfitting. Two common types of pooling layers are max pooling and average
pooling. If we use a max pool with 2 x 2 filters and stride 2, the resultant volume will be
of dimension 16x16x12.
● Fully-Connected Layer: This layer is a regular neural network layer that takes input from
the previous layer and computes the class scores and outputs the 1-D array of size equal
to the number of classes.
Advantages Or Disadvantages:
Advantages of Convolutional Neural Networks (CNNs):
● Good at detecting patterns and features in images, videos and audio signals.
● Robust to translation, rotation and scaling invariance.
Learning
UNIT - III
Convolutional Neural Network
Notes
Introduction: Convolutional Neural Network (CNN)
A Convolutional Neural Network (CNN) is a type of Deep Learning architecture commonly used
for image classification and recognition tasks. It consists of multiple layers, including
Convolutional layers, Pooling layers, and fully connected layers. The Convolutional layer applies
filters to the input image to extract features, the Pooling layer downsamples the image to
reduce computation, and the fully connected layer makes the final prediction. The network
learns the optimal filters through backpropagation and gradient descent.
Artificial Neural Networks are used in various classification tasks like image, audio, words.
Different types of Neural Networks are used for different purposes, for example for predicting
the sequence of words we use Recurrent Neural Networks more precisely an LSTM, similarly for
image classification we use Convolution Neural networks.
Convolution Neural Network
Convolution Neural Networks or covnets are neural networks that share their parameters.
Imagine you have an image. It can be represented as a cuboid having its length, width
(dimension of the image), and height (as images generally have red, green, and blue channels).
,Now imagine taking a small patch of this image and running a small neural network on it, with
say, k outputs and represent them vertically. Now slide that neural network across the whole
image, as a result, we will get another image with different width, height, and depth. Instead of
just R, G, and B channels now we have more channels but lesser width and height. This
operation is called Convolution. If the patch size is the same as that of the image it will be a
regular neural network. Because of this small patch, we have fewer weights.
Now let’s talk about a bit of mathematics that is involved in the whole convolution process.
● Convolution layers consist of a set of learnable filters (a patch in the above image). Every
filter has small width and height and the same depth as that of input volume (3 if the
input layer is image input).
● For example, if we have to run convolution on an image with dimension 34x34x3. The
possible size of filters can be axax3, where ‘a’ can be 3, 5, 7, etc but small as compared to
image dimension.
● During forward pass, we slide each filter across the whole input volume step by step
where each step is called stride (which can have value 2 or 3 or even 4 for high
dimensional images) and compute the dot product between the weights of filters and
patch from input volume.
● As we slide our filters we’ll get a 2-D output for each filter and we’ll stack them together
and as a result, we’ll get output volume having a depth equal to the number of filters.
The network will learn all the filters.
●
Layers used to build ConvNets : A covnets is a sequence of layers, and every layer transforms
one volume to another through a differentiable function.
, Types of layers:
Let’s take an example by running a covnets on of image of dimension 32 x 32 x 3.
● Input Layer: This layer holds the raw input of the image with width 32, height 32, and
depth 3.
● Convolution Layer: This layer computes the output volume by computing the dot
product between all filters and image patches. Suppose we use a total of 12 filters for
this layer we’ll get output volume of dimension 32 x 32 x 12.
● Activation Function Layer: This layer will apply an element-wise activation function to
the output of the convolution layer. Some common activation functions are RELU:
max(0, x), Sigmoid: 1/(1+e^-x), Tanh, Leaky RELU, etc. The volume remains unchanged
hence output volume will have dimension 32 x 32 x 12.
● Pool Layer: This layer is periodically inserted in the covnets and its main function is to
reduce the size of volume which makes the computation fast reduces memory and also
prevents overfitting. Two common types of pooling layers are max pooling and average
pooling. If we use a max pool with 2 x 2 filters and stride 2, the resultant volume will be
of dimension 16x16x12.
● Fully-Connected Layer: This layer is a regular neural network layer that takes input from
the previous layer and computes the class scores and outputs the 1-D array of size equal
to the number of classes.
Advantages Or Disadvantages:
Advantages of Convolutional Neural Networks (CNNs):
● Good at detecting patterns and features in images, videos and audio signals.
● Robust to translation, rotation and scaling invariance.
