Hybrid Deep Learning Algorithms for Dog Breed Identification—A Comparative Analysis

Deep learning and computer vision algorithms will be applied to find the breed of the dog from an image. The goal is to have the user submit an image of a dog, and the model will choose one of the 120 breeds stated in the dataset to determine the dog’s breed. The proposed work uses various deep learning algorithms like Xception, VGG19, NASNetMobile, EfficientNetV2M, ResNet152V2, Hybrid of Inception &Xception, and Hybrid of EfficientNetV2M, NASNetMobile, Inception &Xception to predict dog breeds. ResNet101, ResNet50, InceptionResNetV2, and Inception-v3 on the Stanford Dogs Standard Datasetswere used in the existing system. The proposed models are considered a hybrid of Inception-v3 &Xception and a hybrid of EfficientNetV2M, NASNetMobile, Inception & Xception. This hybrid model outperforms single models like Xception, VGG19, InceptionV3, ResNet50, and ResNet101.The authors used a transfer learning algorithm with data augmentation to increase their accuracy and achieved a validation accuracy score of 71.63% for ResNet101, 63.78% for ResNet50, 40.72% for InceptionResNetV2, and 34.84% for InceptionV3. This paper compares the proposed algorithms with existing ones like ResNet101, ResNet50, InceptionResNetV2, and InceptionV3. In the existing system, ResNet101 gave the highest accuracy of 71.63%. The proposed algorithms give a validation accuracy score of 91.9% for Xception, 55% for VGG19, 83.47% for NASNetMobile, 89.05% for EfficientNetV2M, 87.38% for ResNet152V2, 92.4% for Hybrid of Inception-v3 &Xception, and 89.00% for Hybrid of EfficientNetV2M, NASNetMobile, Inception &Xception. Among these algorithms, the Hybrid of Inception-v3 &Xception gives the highest accuracy of 92.4%.