A computer-implemented method, the method comprising: extracting a plurality of first groups of layers (410, 420) from a first convolutional neural network, CNN, model, each group of the plurality of first groups having a first convolutional layer (404, 414), a first batch-norm layer (406, 416), and a first scaling layer (408, 418); for each group of the plurality of first groups, calculating a first scale vector and a first shift vector based on the first batch-norm layer in the form of scale vector = 1 std y 1; shift vector = − mean y 1 std y 1 y 1 is an input of the first batch-norm layer, mean(y 1) is a mean of y 1, std(y 1) is a standard deviation of y 1, and generating a second convolutional layer (424, 428) representing the corresponding group of the plurality of first groups based on the first convolutional layer, the first scale vector, the first shift vector, and the first scaling layer, a weight α 0 of the second convolutional layer and a bias β 0 of the second convolutional layer are calculated in the form of α o = α 1 α 2 std y 1; and β o = β 2 + α 2 β 1 std y 1 − α 2 mean y 1 std y 1 α 1 is a weight of the first convolutional layer, β 1 is a bias of the first convolutional layer, α 2 is a scaling factor of the scaling layer, and β 2 is a shift factor of the scaling layer, y 1 is an output of the first convolutional layer, mean(y 1) is the mean of y 1, std(y 1) is the standard deviation of y 1; and generating a second CNN model based on the second convolutional layer corresponding to the plurality of the first groups, wherein the second CNN model is utilized subsequently to classify an object perceived by an autonomous driving vehicle, ADV. extracting a plurality of first groups of layers (410, 420) from a first convolutional neural network, CNN, model, each group of the plurality of first groups having a first convolutional layer (404, 414), a first batch-norm layer (406, 416), and a first scaling layer (408, 418); for each group of the plurality of first groups, calculating a first scale vector and a first shift vector based on the first batch-norm layer in the form of scale vector = 1 std y 1; shift vector = − mean y 1 std y 1 y 1 is an input of the first batch-norm layer, mean(y 1) is a mean of y 1, std(y 1) is a standard deviation of y 1, and generating a second convolutional layer (424, 428) representing the corresponding group of the plurality of first groups based on the first convolutional layer, the first scale vector, the first shift vector, and the first scaling layer, a weight α 0 of the second convolutional layer and a bias β 0 of the second convolutional layer are calculated in the form of α o = α 1 α 2 std y 1; and β o = β 2 + α 2 β 1 std y 1 − α 2 mean y 1 std y 1 α 1 is a weight of the first convolutional layer, β 1 is a bias of the first convolutional layer, α 2 is a scaling factor of the scaling layer, and β 2 is a shift factor of the scaling layer, y 1 is an output of the first convolutional layer, mean(y 1) is the mean of y 1, std(y 1) is the standard deviation of y 1; and generating a second CNN model based on the second convolutional layer corresponding to the plurality of the first groups, wherein the second CNN model is utilized subsequently to classify an object perceived by an autonomous driving vehicle, ADV.