EfficientNet-L2

EfficientNet-L2 is a neural network architecture developed by Google researchers in 2019. It is part of the EfficientNet family of models, which are designed to achieve state-of-the-art performance on image classification tasks while being highly efficient in terms of computational resources required.

EfficientNet-L2 is the largest and most powerful model in the EfficientNet family, with over 480 million parameters. It was trained on the ImageNet dataset, which consists of over 1.4 million images across 1,000 classes.

Compared to other state-of-the-art models such as ResNet-152 and Inception-ResNet-v2, EfficientNet-L2 achieves significantly better accuracy on the ImageNet benchmark while requiring less computation and memory. This makes it a highly efficient and effective model for a wide range of computer vision tasks.