EfficientNet-L2 vs NASNet: Imagenet Performance Comparison

EfficientNet-L2 and NASNet are convolutional neural network models trained and tested on the Imagenet dataset. Here's a comparison of their performance:

1. Parameter Count: EfficientNet-L2 has 480M parameters, while NASNet has 88M parameters. EfficientNet-L2 has significantly more parameters.

2. Accuracy: On Imagenet, EfficientNet-L2 achieves a Top-1 accuracy of 90.5% and a Top-5 accuracy of 98.1%. NASNet, on the other hand, achieves a Top-1 accuracy of 82.7% and a Top-5 accuracy of 96.2%. Therefore, EfficientNet-L2 exhibits significantly higher accuracy.

3. Training Speed: Due to its larger parameter count, EfficientNet-L2 is expected to train slower. However, EfficientNet-L2 employs acceleration techniques like MBConv blocks and Swish activation, making its training speed faster than other models of similar size.

4. Inference Speed: EfficientNet-L2 also performs better in inference speed. It utilizes lightweight structures like Depthwise Separable convolutions and Squeeze-and-Excitation modules, which reduce computational cost without compromising accuracy.

In conclusion, although EfficientNet-L2 has a larger parameter count, it surpasses NASNet in terms of accuracy and inference speed. Therefore, for large-scale image classification tasks, EfficientNet-L2 presents a more favorable choice.