A computer-implemented method for image classification of digital images, said method comprising a first phase of compressing a convolutional neural network comprising a plurality of layers, each layer comprising a weight tensor whose shape is a vector with components referred to as "size numbers", said first phase being performed using a rank hyperparameter R' and a size hyperparameter B representative of a number of basis tensors, each basis tensor being an array belonging to a vector space whose dimension is equal to R' x N' x R', where N' is a number equal to or larger than 2, said first phase comprising, for each weight tensor, a set of steps of: - embedding (E20) the weight tensor into an envelope tensor, the size of the envelope tensor being equal to a product of size numbers such that: • if a size number M of the weight tensor is an integer power of said number N', said size number M is a size number of the envelope tensor, • if a size number M of the weight tensor is not an integer power of said number N', said number N' raised to a power equal to the ceiling value of log base N' of said size number M is a size number of the envelope tensor, - reshaping (E30) said envelope tensor by tensorization based on said number N' such that the shape vector of the reshaped envelope tensor comprises d_I occurences of N', - determining (E40) a Tensor-Ring representation of the reshaped envelope tensor in the form of a sequence of d_I elements comprising tensors, said tensors of the sequence being referred to as "cores" and belonging to said vector space, - parameterizing (E50) each core into a combination of the basis tensors, the method further comprising a second phase comprising the steps of: - initializing (F20) the basis tensors and the combinations from which said compressed network is parameterized, - training (F30) said compressed network, the basis tensors and coefficients defining said combinations being considered as trainable parameters during said training, - classifying digital images using the trained compressed network. - embedding (E20) the weight tensor into an envelope tensor, the size of the envelope tensor being equal to a product of size numbers such that: • if a size number M of the weight tensor is an integer power of said number N', said size number M is a size number of the envelope tensor, • if a size number M of the weight tensor is not an integer power of said number N', said number N' raised to a power equal to the ceiling value of log base N' of said size number M is a size number of the envelope tensor, • if a size number M of the weight tensor is an integer power of said number N', said size number M is a size number of the envelope tensor, • if a size number M of the weight tensor is not an integer power of said number N', said number N' raised to a power equal to the ceiling value of log base N' of said size number M is a size number of the envelope tensor, - reshaping (E30) said envelope tensor by tensorization based on said number N' such that the shape vector of the reshaped envelope tensor comprises d_I occurences of N', - determining (E40) a Tensor-Ring representation of the reshaped envelope tensor in the form of a sequence of d_I elements comprising tensors, said tensors of the sequence being referred to as "cores" and belonging to said vector space, - parameterizing (E50) each core into a combination of the basis tensors, the method further comprising a second phase comprising the steps of: - initializing (F20) the basis tensors and the combinations from which said compressed network is parameterized, - training (F30) said compressed network, the basis tensors and coefficients defining said combinations being considered as trainable parameters during said training, - classifying digital images using the trained compressed network. <EOS>