  Designing light-weight CNN models with little parameters and Flops is a
prominent research concern. However, three significant issues persist in the
current light-weight CNNs: i) the lack of architectural consistency leads to
redundancy and hindered capacity comparison, as well as the ambiguity in
causation between architectural choices and performance enhancement; ii) the
utilization of a single-branch depth-wise convolution compromises the model
representational capacity; iii) the depth-wise convolutions account for large
proportions of parameters and Flops, while lacking efficient method to make
them light-weight. To address these issues, we factorize the four vital
components of light-weight CNNs from coarse to fine and redesign them: i) we
design a light-weight overall architecture termed LightNet, which obtains
better performance by simply implementing the basic blocks of other
light-weight CNNs; ii) we abstract a Meta Light Block, which consists of
spatial operator and channel operator and uniformly describes current basic
blocks; iii) we raise RepSO which constructs multiple spatial operator branches
to enhance the representational ability; iv) we raise the concept of receptive
range, guided by which we raise RefCO to sparsely factorize the channel
operator. Based on above four vital components, we raise a novel light-weight
CNN model termed as FalconNet. Experimental results validate that FalconNet can
achieve higher accuracy with lower number of parameters and Flops compared to
existing light-weight CNNs.
