A computer implemented method for learning parameters of a CNN for image recognition, the method comprising the steps of: (a) (i) generating, by cropping and resizing areas of each of N ROIs on a training image, N ROI images, (ii) generating, via one or more convolutional layers, an ROI feature map per each of the N ROIs by applying one or more convolution operations to each of the N ROI images, (iii) generating, via a pooling layer, N pooled feature maps, having a width of M1, a height of M2 and a number of channels of J, per each of the N ROIs by applying one or more pooling operations to the N ROI feature maps, and (iv) generating, via a first transposing layer, an integrated feature map, having a width of N, a height of 1 and a number of channels of M1•M2•J by concatenating each of M1•M2•J pixels per each of the N ROIs along channels of the integrated feature map; (b) (i) generating, via a first reshaping layer, a first reshaped feature map having a width of N, a height of H1 and a number of channels of CEIL CEIL M 1 · M 2 · J H 1 by concatenating each of H1 features in each group comprised of each corresponding H1 channels among all channels of the integrated feature map along the height of H1 of the first reshaped feature map, and (ii) generating, via a 1×H1 convolutional layer having a number of filters of K, a first adjusted feature map having a width of N, a height of 1 and a number of channels of K by applying a 1×H1 convolution operation to the first reshaped feature map, and (i) generating, via a second reshaping layer, a second reshaped feature map having a width of N, a height of H2 and a number of channels of CEIL K H 2 by concatenating each of H2 features in each group comprised of each corresponding H2 channels among all channels of the first adjusted feature map along the height of H2 of the second reshaped feature map, and (ii) generating, via a 1×H2 convolutional layer having a number of filters of L, a second adjusted feature map having a width of N, a height of 1 and a number of channels of L by applying a 1×H2 convolution operation to the second reshaped feature map; and (c) (i) generating, via a second transposing layer, pixel-wise feature maps, a width of 1, a height of 1 and a number of channels of L, per each of the N ROIs by dividing the second adjusted feature map by each of the pixels, and generating, via a classifying layer, object information on each of the N ROIs by using the pixel-wise feature maps per each of the N ROIs, calculating, via a loss layer, one or more object losses by referring to the object information and its corresponding GT, and learning at least part of parameters of the 1×H2 convolutional layer, the 1×H1 convolutional layer, and the convolutional layers by backpropagating the object losses. (a) (i) generating, by cropping and resizing areas of each of N ROIs on a training image, N ROI images, (ii) generating, via one or more convolutional layers, an ROI feature map per each of the N ROIs by applying one or more convolution operations to each of the N ROI images, (iii) generating, via a pooling layer, N pooled feature maps, having a width of M1, a height of M2 and a number of channels of J, per each of the N ROIs by applying one or more pooling operations to the N ROI feature maps, and (iv) generating, via a first transposing layer, an integrated feature map, having a width of N, a height of 1 and a number of channels of M1•M2•J by concatenating each of M1•M2•J pixels per each of the N ROIs along channels of the integrated feature map; (b) (i) generating, via a first reshaping layer, a first reshaped feature map having a width of N, a height of H1 and a number of channels of CEIL CEIL M 1 · M 2 · J H 1 by concatenating each of H1 features in each group comprised of each corresponding H1 channels among all channels of the integrated feature map along the height of H1 of the first reshaped feature map, and (ii) generating, via a 1×H1 convolutional layer having a number of filters of K, a first adjusted feature map having a width of N, a height of 1 and a number of channels of K by applying a 1×H1 convolution operation to the first reshaped feature map, and (i) generating, via a second reshaping layer, a second reshaped feature map having a width of N, a height of H2 and a number of channels of CEIL K H 2 by concatenating each of H2 features in each group comprised of each corresponding H2 channels among all channels of the first adjusted feature map along the height of H2 of the second reshaped feature map, and (ii) generating, via a 1×H2 convolutional layer having a number of filters of L, a second adjusted feature map having a width of N, a height of 1 and a number of channels of L by applying a 1×H2 convolution operation to the second reshaped feature map; and (c) (i) generating, via a second transposing layer, pixel-wise feature maps, a width of 1, a height of 1 and a number of channels of L, per each of the N ROIs by dividing the second adjusted feature map by each of the pixels, and generating, via a classifying layer, object information on each of the N ROIs by using the pixel-wise feature maps per each of the N ROIs, calculating, via a loss layer, one or more object losses by referring to the object information and its corresponding GT, and learning at least part of parameters of the 1×H2 convolutional layer, the 1×H1 convolutional layer, and the convolutional layers by backpropagating the object losses.