As the most basic application and implementation of deep learning, image classification has grown in popularity. Various datasets are provided by renowned data science communities for benchmarking machine learning algorithms and pre-trained models. The ASSIRA Cats & Dogs dataset is one of them and is being used in this research for its overall acceptance and benchmark standards. A comparison of various pre-trained models is demonstrated by using different types of optimizers and loss functions. Hyper-parameters are changed to gain the best result from a model. By applying this approach, we have got higher accuracy without major changes in the training model. To run the experiment, we used three different computer architectures: a laptop equipped with NVIDIA GeForce GTX 1070, a laptop equipped with NVIDIA GeForce RTX 3080Ti, and a desktop equipped with NVIDIA GeForce RTX 3090. The acquired results demonstrate supremacy in terms of accuracy over the previously done experiments on this dataset. From this experiment, the highest accuracy which is 99.65% is gained using the NASNet Large.

翻译：图像分类作为深度学习最基本的应用与实现，其流行度日益增长。知名数据科学社区提供了多种数据集，用于对机器学习算法和预训练模型进行基准测试。ASSIRA猫狗数据集正是其中之一，因其广泛的接受度和基准标准而被本研究采用。通过使用不同类型的优化器和损失函数，展示了多种预训练模型的性能对比。通过调整超参数，获得了模型的最佳结果。采用这种方法，我们在无需对训练模型进行重大改动的情况下取得了更高的准确率。实验过程中，我们使用了三种不同的计算机架构：搭载NVIDIA GeForce GTX 1070的笔记本电脑、搭载NVIDIA GeForce RTX 3080Ti的笔记本电脑以及搭载NVIDIA GeForce RTX 3090的台式机。获得的结果表明，在准确率方面优于先前在该数据集上完成的实验。本次实验中，使用NASNet Large模型达到了最高准确率99.65%。