<p>Computer Vision is a mathematical
tool formulated to extend human vision to machines. This tool can perform
various tasks such as object classification, object tracking, motion
estimation, and image segmentation. These tasks find their use in many applications,
namely robotics, self-driving cars, augmented reality, and mobile applications.
However, opposed to the traditional technique of incorporating handcrafted
features to understand images, convolution neural networks are being used to
perform the same function. Computer vision applications widely use CNNs due to
their stellar performance in interpreting images. Over the years, there have
been numerous advancements in machine learning, particularly to CNNs. However,
the need to improve their accuracy, model size and complexity increased, making
their deployment in restricted environments a challenge. Many researchers
proposed techniques to reduce the size of CNN while still retaining its
accuracy. Few of these include network quantization, pruning, low rank, and
sparse decomposition and knowledge distillation. Some methods developed
efficient models from scratch. This thesis achieves a similar goal using design
space exploration techniques on the latest variant of MobileNets, MobileNet V3.
Using Depthwise Pointwise Depthwise (DPD) blocks, escalation in the number of
expansion filters in some layers and mish activation function MobileNet V3 is
reduced to 84.96% in size and made 0.2% more accurate. Furthermore, it is
deployed in NXP i.MX RT1060 for image classification on CIFAR-10 dataset.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14442710 |
| Date | 10 May 2021 |
| Creators | Kavyashree Pras Shalini Pradeep Prasad (10662020) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/COMPRESSED_MOBILENET_V3_AN_EFFICIENT_CNN_FOR_RESOURCE_CONSTRAINED_PLATFORMS/14442710 |
Page generated in 0.2998 seconds