Fine Grain Image Classification Using Fine-Tuned MobileNet Model

The paper focuses on the development of an image classifier, which classifies various fine-grained floral images based on their species. Classification of flowers has many advantages in various fields like Science, Medical, Industries etc. By classifying various types of flowers, one can study their characteristics, etiquette, considering to our knowledge of ecosystems and plant life. Our focal point rests upon the image features woven into the pixels. Through various Convolutional neural network models, the work is able to propose an efficient model based on fine-tuned MobileNetv3 architecture and sel-attention mechanismsthat classifies fine-grained flower images into their respective species.