A device for image classification comprising a convolutional neural network, wherein the device is configured to receive an image captured by a camera, the image comprising a plurality of pixels, the convolutional neural network is configured to generate a plurality of probability values, each probability value being linked to a respective one of a plurality of predetermined classes and indicating the probability that the image or a pixel of the image is associated with the respective class, and the convolutional neural network comprises a plurality of consecutively arranged convolutional blocks (20 1, 20 2, 20 3) and each of the convolutional blocks (20 1, 20 2, 20 3) comprises four consecutively arranged convolutional layers (21, 22, 23, 24): a first convolutional layer (21 1, 21 2, 21 3) configured to perform a pointwise convolution using a first kernel, a second convolutional layer (22 1, 22 2, 22 3) configured to perform a depthwise convolution using a second kernel, wherein the second kernel has one of a single row and a single column, a third convolutional layer (23 1, 23 2, 23 3) configured to perform a depthwise convolution using a third kernel, wherein the third kernel has a single column if the second kernel has a single row, and the third kernel has a single row if the second kernel has a single column, and a fourth convolutional layer (24 1, 24 2, 24 3) configured to perform a convolution using a fourth kernel, wherein the number of output channels of the fourth convolutional layer (24 1, 24 2, 24 3) is twice the number of the output channels of the first convolutional layer (21 1, 21 2, 21 3). the device is configured to receive an image captured by a camera, the image comprising a plurality of pixels, the convolutional neural network is configured to generate a plurality of probability values, each probability value being linked to a respective one of a plurality of predetermined classes and indicating the probability that the image or a pixel of the image is associated with the respective class, and the convolutional neural network comprises a plurality of consecutively arranged convolutional blocks (20 1, 20 2, 20 3) and each of the convolutional blocks (20 1, 20 2, 20 3) comprises four consecutively arranged convolutional layers (21, 22, 23, 24): a first convolutional layer (21 1, 21 2, 21 3) configured to perform a pointwise convolution using a first kernel, a second convolutional layer (22 1, 22 2, 22 3) configured to perform a depthwise convolution using a second kernel, wherein the second kernel has one of a single row and a single column, a third convolutional layer (23 1, 23 2, 23 3) configured to perform a depthwise convolution using a third kernel, wherein the third kernel has a single column if the second kernel has a single row, and the third kernel has a single row if the second kernel has a single column, and a fourth convolutional layer (24 1, 24 2, 24 3) configured to perform a convolution using a fourth kernel, wherein the number of output channels of the fourth convolutional layer (24 1, 24 2, 24 3) is twice the number of the output channels of the first convolutional layer (21 1, 21 2, 21 3). a first convolutional layer (21 1, 21 2, 21 3) configured to perform a pointwise convolution using a first kernel, a second convolutional layer (22 1, 22 2, 22 3) configured to perform a depthwise convolution using a second kernel, wherein the second kernel has one of a single row and a single column, a third convolutional layer (23 1, 23 2, 23 3) configured to perform a depthwise convolution using a third kernel, wherein the third kernel has a single column if the second kernel has a single row, and the third kernel has a single row if the second kernel has a single column, and a fourth convolutional layer (24 1, 24 2, 24 3) configured to perform a convolution using a fourth kernel, wherein the number of output channels of the fourth convolutional layer (24 1, 24 2, 24 3) is twice the number of the output channels of the first convolutional layer (21 1, 21 2, 21 3).