In this research study, DNN are used to classify the images.With 30 various varieties of Balls used for this paper, the input data is mostly concentrated on the Balls category. Deep neural networks were chosen as the best alternative due to their high percentage of accuracy during the training phase. The percentage accuracy of image classification is used to describe the results. When the accuracy is around 89%, fine-tuning is used to increase the accuracy to 99% We spent a lot of time understanding the dataset and determining the shape and accuracy of each feature.collected information on 30 different feature types. 5 shapes were trained on each of the 3495 images in the 30 subdirectories for each type of feature.