Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA
  Cats and Dogs Dataset
Galib Muhammad Shahriar Himel Md. Masudul Islam
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.