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Computer vision is an area of machine learning that enables computers and systems to take actions and make recommendations based on information from digital images, videos, and other visual inputs. The purpose of this paper is to discuss the selection of optimal parameters in car classification training. This paper discusses computer vision training using the CNN algorithm on vehicle miniature classification. One of the factors that affect the performance of Convolutional Neural Network (CNN) is the selection of appropriate parameters. The data we use comes from the primary data we collect. We took photos of 3 types of vehicles, namely buses, cars, and motorcycles with a total of 486 instances. Next we do data preprocessing by resizing 256*256px. Then we conducted various trials of data augmentation i.e: including "horizontal", "vertical", "horizontal and vertical". We experimented with different augmentations to get the highest accuracy. We present several variations of the epoch to identify the best model of CNN. The accuracy results obtained are 97.34%. [ABSTRACT FROM AUTHOR]

The major advancing techniques in machine learning are mainly two, they are deep learning and computer vision. The advanced deep learning techniques are highly promising to increase the interest in research within the upcoming years. This is often because the eminent benefits in overcoming the drawbacks within the outdated techniques for producing the result accurately. The theme of this paper is to provide a comprehensive description on the convolution neural network and its recent improvements which includes the CNN – S convolution neural network segmentation, CNN – CBIR convolution neural network – content-based image retrieval system. This survey paper provides a detailed summary within the latest advancements in the domain of CNN with various extended applications through its classification for improved understanding. Analysing the performance is done considering the speed, accuracy and ease. [ABSTRACT FROM AUTHOR]