A deep convolutional neural network architecture for image classification

ENGLISH SUMMARY : Convolutional Neural Networks (CNNs), a specialised form of Neural Networks (NNs), are wellknown for their state-of-the-art results obtained in Computer Vision (CV) and Deep Learning (DL) tasks throughout the past few years. Some of the exciting application areas of CNNs include image classi cation, object detection, video processing, natural language processing, and speech recognition. The powerful learning ability of deep CNNs is primarily owed to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in hardware technology has accelerated the research done in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of di erent activation and loss functions, parameter optimisation, regularisation, and architectural innovations by using di erent layer structures. Therefore, the objective of this study is based on image classi cation and object detection tasks, that is, creating custom-designed CNN architectures for deployment on real-world datasets while comparing these custom-designed architectures to those state-of-the-art architectures found in literature while comparing di erent optimisation procedures and activations functions. All major developments of CNNs are discussed and critically considered, with a view to improve CNNs in the context of the number of parameters used to obtain satisfactory results and additionally to obtain a better understanding of the term known as a `black box' which is usually associated with CNNs such that they are complex models with little understanding in the way how their classi cations are done. The most promising modern CNN architectures with associated hyperparameters are further explored by means of empirical work. Evaluation is done on the validity of ndings reported in the literature and comments are made on the e ectiveness of recent proposals through the use of ve di erent real-world datasets. The empirical work done will be complemented by additional coded notebooks that could be used to implement state-of-the-art techniques, as well as for comparative and model assessment experiments.