Classify Tigers in Machine Learning Approach

Abstract

The classification of tiger images in this research uses the Tensor Flow framework and deep learning, which is also known as machine learning. Python is used as a programming language because of how successfully it functions. In this study, three (3) different kinds of tigers were used, and this is where the input data is mostly addressed. It has been decided that this method is the optimal one for the training process because of the high percentage of accuracy it attained. In the results, it is discussed how accurate the image classification was in percentage terms. Similar results apply to another type of tiger, where the average result is up to 90% and higher. The majority of the image receives 90%.Classifier is the method of determining what an image shows. An image classifier is trained to identify various image classes. For instance, you could instruct a model to identify pictures of multiple distinct animal species.Its procedure of graphically picking tests (response variable) in an image and attributing those to or before subgroups (for example, that can be used for the entire photo. Classification technique and path length are multiple common techniques for categorizing the entire image using classification model. ' categorization scientific research the data characteristics by first computing.