The EfficientNet model is a state-of-the-art Convolutional Neural Network (CNN) architecture that has achieved outstanding performance on various computer vision tasks. In this project, the EfficientNet model was implemented from scratch using PyTorch. The CNN and Inverted-Residual blocks were built to scale the model with the expand ratios and phi values mentioned in the original paper.
The EfficientNet model is a powerful and efficient CNN architecture that has revolutionized the field of computer vision. This project involved the implementation of the EfficientNet model from scratch using PyTorch, which allowed for a deeper understanding of the model's architecture and working principles. The CNN and Inverted-Residual blocks were built to scale the model with expand ratios and phi values as mentioned in the original paper. Overall, this project demonstrates the potential of Deep Learning frameworks to replicate state-of-the-art models and provides a valuable contribution to the field of computer vision.