You work as a machine learning specialist for a large auto parts manufacturing company.
You have been tasked with building a machine learning model to analyze images of car parts on your company's production lines to classify the parts automatically.
The classified parts will then be placed in their appropriate warehouse containers by classification. Some examples of the classifications are: electronics, trim, gasket, hose, etc.
Since your company has many manufacturing plants across the globe, your classification model needs to be able to classify millions of high-resolution images. Which algorithm best fits your problem?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
The Object Detection algorithm is used to identify all instances of an object within an image.
While this may be used in a naive approach to the image classification problem, it is not meant for image classification in the way and scale needed for your problem.
Option B is correct.
The SageMaker built-in Image Classification algorithm uses a Convolutional Neural Network to classify images that support multi-label classification.
It scales to millions of images at high resolution.
It solves this problem through convolution and multiple layers in the neural network.
(See the article AWS SageMaker and CNN for Dog Breed Classification)
Option C is incorrect.
The Latent Dirichlet Allocation algorithm is used for topic discovery within documents.
Option D is incorrect.
The Factorization Machine algorithm can be used to classify observations, but it is used primarily to detect interactions between features.
Examples include reactions to ads on a web page or item recommendations.
Reference:
Please see the SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms.
The best algorithm to classify millions of high-resolution images of car parts on your company's production lines is Image Classification.
Image classification is a type of supervised learning where the machine learning model learns to classify input images into different classes or categories. In this case, the classes could be electronics, trim, gasket, hose, etc. The model learns from labeled training data, which means that the images are already labeled with the correct classification.
Object detection is another type of supervised learning where the model learns to detect and locate objects within an image, but it is not the best fit for this problem. Object detection is better suited for scenarios where the machine learning model needs to detect and locate multiple objects within an image and classify each object.
Latent Dirichlet Allocation (LDA) is an unsupervised learning algorithm commonly used in natural language processing and topic modeling. LDA is not well-suited for image classification tasks.
Factorization Machine is a type of supervised learning algorithm used for recommendation systems and regression tasks. It is not the best fit for image classification tasks.
In summary, the best algorithm for classifying millions of high-resolution images of car parts on your company's production lines is Image Classification. It is a supervised learning algorithm that learns to classify input images into different classes or categories.