Deep Learning and Convolutional Neural Networks - Report Writing - IT Assignment Help

Assignment Task

Assignment Objectives
The purpose of this assignment is to demonstrate competence in the following skills.
- To ensure that the student has a firm understanding of CNNs and object detections algorithms. This will facilitate the learning of advanced topics for research and also assist in completing the project.
- To ensure that the student can develop custom CNN architectures for different computer vision related tasks.

Tasks:
Description:
1. Customize AlexNet/GoogleNet/ResNet etc. and reduce/increase the layers, Train and test for image classification.
2. Implement the Faster-RCNN and SDD architectures for object detection/localization. (Use of existing implementation such as Google Object detection API is permitted).
3. Train and test on the given dataset for object detection, using Faster-RCNN and SSD object detection methods.

Datasets for each tasks will be provided.
Write a short report on the implementation, linking the concepts and methods learned in class, and also provide assumptions/intuitions considered to create the custom CNNs for image classification. Provide diagrams for the CNNs architecture where required for better illustrations. Provide the model summary, such as input and output parameters, etc. Discuss the results clearly and explain the different situations/constraints for the better understanding of the results obtained.

Dataset to be used: Provided separately (Check Canvas under Assignment - Assignment-2).

Report Structure (suggestion only):
The report may include the following sections:
1. Introduction: Provide a brief outline of the report and also briefly explain the baseline CNN architectures used to create the custom CNNs for image classification. Also mention about the object detection methods used.
2. Dataset: Provide a brief description of the dataset used with some sample images of each class.
3. Proposed CNN architecture for Image classification:
a. Baseline architecture used.
b. Customized architecture
c. Assumptions/intuitions
d. Model summary 

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