  Convolution Neural Networks (CNN) have performed well in many applications
such as object detection, pattern recognition, video surveillance and so on.
CNN carryout feature extraction on labelled data to perform classification.
Multi-label classification assigns more than one label to a particular data
sample in a data set. In multi-label classification, properties of a data point
that are considered to be mutually exclusive are classified. However, existing
multi-label classification requires some form of data pre-processing that
involves image training data cropping or image tiling. The computation and
memory requirement of these multi-label CNN models makes their deployment on
edge devices challenging. In this paper, we propose a methodology that solves
this problem by extending the capability of existing multi-label classification
and provide models with lower latency that requires smaller memory size when
deployed on edge devices. We make use of a single CNN model designed with
multiple loss layers and multiple accuracy layers. This methodology is tested
on state-of-the-art deep learning algorithms such as AlexNet, GoogleNet and
SqueezeNet using the Stanford Cars Dataset and deployed on Raspberry Pi3. From
the results the proposed methodology achieves comparable accuracy with 1.8x
less MACC operation, 0.97x reduction in latency and 0.5x, 0.84x and 0.97x
reduction in size for the generated AlexNet, GoogleNet and SqueezeNet CNN
models respectively when compared to conventional ways of achieving multi-label
classification like hard-coding multi-label instances into single labels. The
methodology also yields CNN models that achieve 50\% less MACC operations, 50%
reduction in latency and size of generated versions of AlexNet, GoogleNet and
SqueezeNet respectively when compared to conventional ways using 2 different
single-labelled models to achieve multi-label classification.
