Omni-scale convolution for convolutional neural networks

At least one non-transitory machine readable storage medium comprising instructions that, when executed, cause at least one processor to perform operations including:

• implementing a convolution operation in one or more convolutional layers of a convolutional neural network (CNN), including at least:
• applying a plurality of dilation rates in a plurality of kernels of a kernel lattice of a convolutional layer of the one or more convolutional layers, and
• applying a cyclic pattern for the plurality of dilation rates in the plurality of kernels of the convolutional layer;
• receiving a set of input data for processing by the CNN;
• and
• utilizing the CNN to generate an output, including applying the plurality of dilation rates according to the cyclic pattern in the one or more convolutional layers.

A system comprising:

• one or more processors to process data, including computer vision processing utilizing a convolutional neural network (CNN), the CNN including one or more convolutional layers;
• a memory to store data, including data for CNN processing;
• and
• an omni-convolution tool to provide support for objection recognition by the CNN in varying scales of object sizes;
• wherein application of the omni-convolution tool includes at least:
• applying a plurality of dilation rates in a plurality of kernels of a kernel lattice of a convolution layer of the one or more convolutional layers of the CNN, and
• applying a cyclic pattern for the plurality of dilation rates in the plurality of kernels of the convolutional layer.