Flower Classification using a Transfer-based Model

The flower classification problem involves identifying the species of a given flower image. There were several challenges faced by existing technologies for flower classification like overfitting, computational complexity, limited accuracy, and parameter tuning. In this research, a deep learning model based on Xception architecture is proposed to solve this problem. The proposed model consists of multiple Xception blocks, each of which has a convolutional layer followed by a residual connection and a series of other operations. The output of the final Xception block is fed into a fully connected layer to obtain the final classification. The model was trained on a large dataset of flower images and achieved high accuracy on the test set. The proposed model also conducted experiments to evaluate the performance of the model under various conditions, such as different input resolutions and different amounts of training data. The results show that the proposed model outperforms state-of-the-art methods on the flower classification task. It demonstrates the accuracy of 99.48% and the effectiveness of using the xception architecture in deep learning for image classification tasks and highlights the importance of proper data pre-processing and augmentation techniques in achieving good performance.