Nowadays, several methodologies and algorithms are being developed to improve image recognition and increase efficiency in species identification. In this context, this paper introduces a comparative analysis of the efficiency between different datasets in the identification of dogs and cats in a convolutional neural network (CNN). In this study, a convolutional neural network architecture with 13 layers was evaluated using three different datasets. In the first set, the 'catsvsdogs' database from TensorFlow was used. In the second CNNN, the network was trained using a set of images that included only dog2 and cat2 species. Finally, in the third CNN the network was trained using a set of images that included only dog3 and cat3 species. The hypothesis put forward is that training a convolutional neural network with customized images of specific dogs and cats improves the accuracy in ident...