Aiming at the image classification issue of the"dying neuron"problem associated with its ReLU ac-tivation function and the challenge of imbalanced test samples,a modified version of the VGG 16 model,named LBF-VGG 16(Leaky-Batch-Focal-VGG 16),is proposed.This enhanced model substitutes the tra-ditional ReLU activation function with Leaky ReLU and integrates Batch Normalization layers between the line-ar and nonlinear components to foster better convergence.During the training phase,the Stochastic Gradient Descent optimizer is employed in conjunction with Focal Loss.Comparative experiments were conducted in two groups:one contrasting VGG 16 with LBF-VGG 16,and the other comparing VGG 13+Local,VGG 19+Focal Loss,and LBF-VGG 16.The outcomes demonstrate that the LBF-VGG 16 model outperforms the other models in terms of accuracy and convergence speed.
关键词
图像分类/计算机视觉/VGG16/Leaky/Relu/Focal/Loss
Key words
image classification/computer vision/VGG16/Leaky Relu/Focal Loss
维普
万方数据