Learn how a deep convolutional neural network (CNN) with a sigmoid output layer accurately detects the presence of cars in images for binary image classification tasks.
Table of Contents
Question
A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.
Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?
A. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
B. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
C. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
Answer
D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
Explanation
The most suitable architecture for accurately detecting the presence of a car in an image is a deep convolutional neural network (CNN) classifier with the images as input and a sigmoid output layer that outputs the probability of a car’s presence.
CNNs excel at processing grid-like data such as images due to their ability to capture spatial hierarchies of features through convolutions and pooling operations. They can effectively learn and extract relevant features from the raw pixel data.
Using a sigmoid output layer with a single neuron is appropriate for binary classification tasks like determining the presence or absence of a car. The sigmoid activation function squashes the output between 0 and 1, representing the probability of the positive class (car present).
While a softmax output layer is commonly used for multi-class classification, it is not the best choice for binary classification. Softmax normalizes the outputs to represent a probability distribution over multiple classes, which is unnecessary for a binary problem.
An MLP, although capable of learning patterns, lacks the spatial inductive biases of CNNs and may not be as effective for image-based tasks.
In summary, a deep CNN with a sigmoid output layer is the most suitable architecture for accurately detecting the presence of a car in an image.
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