  The paradigm of automated waste classification has recently seen a shift in
the domain of interest from conventional image processing techniques to
powerful computer vision algorithms known as convolutional neural networks
(CNN). Historically, CNNs have demonstrated a strong dependency on powerful
hardware for real-time classification, yet the need for deployment on weaker
embedded devices is greater than ever. The work in this paper proposes a
methodology for reconstructing and tuning conventional image classification
models, using EfficientNets, to decrease their parameterisation with no
trade-off in model accuracy and develops a pipeline through TensorRT for
accelerating such models to run at real-time on an NVIDIA Jetson Nano embedded
device. The train-deployment discrepancy, relating how poor data augmentation
leads to a discrepancy in model accuracy between training and deployment, is
often neglected in many papers and thus the work is extended by analysing and
evaluating the impact real world perturbations had on model accuracy once
deployed. The scope of the work concerns developing a more efficient variant of
WasteNet, a collaborative recycling classification model. The newly developed
model scores a test-set accuracy of 95.8% with a real world accuracy of 95%, a
14% increase over the original. Our acceleration pipeline boosted model
throughput by 750% to 24 inferences per second on the Jetson Nano and real-time
latency of the system was verified through servomotor latency analysis.
