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

Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset