Project 3

Overview

The objective of this project is to gain experience building and training convolutional neural networks for classificaiton and detection. In this project you will implement a feed forward CNN for image classification and a version of Faster R-CNN for object detection.

The goals for this project are as follows:

• Implement the forward and backward pass for a convolutional neural network.
• Apply your network implementation to image classification.
• Observe improved classification performance using convolutions.
• Implement the Faster R-CNN architecture for object detection.
• Understand the characteristics of neural network based object detection using the PROPS Detection Dataset.

Instructions

1. Download the project starter code
   • Project 3 starter code: P3.zip
2. Unzip the starter code and upload to Google Drive
   • Once unzipped, you should find a root directory titled ‘P3’. The ‘P3’ directory contains all starter code and files needed to complete this project. Please upload the ‘P3’ directory to your Google Drive.
3. Open the *.ipynb and *.py files and implement features

   • We recommend implementing the features in a Google Colab environment. The Colab development environment can be accessed by double-clicking on each *.ipynb and *.py file within your Drive. Instructions for each feature are included in the convolutional_networks.ipynb and two_stage_detector.ipynb files.

   • We suggest starting by implementing the required features as they appear in the convolutional_networks.ipynb notebook, which can be thought of as part 1 of the project. Then work through the two_stage_detector.ipynb notebook as part 2 of the project.

   • While working on the project, keep the following in mind:

     • The notebook and the python file have clearly marked blocks where you are expected to write code. Do not write or modify any code outside of these blocks.
     • Do not add or delete cells from the notebook. You may add new cells to perform scratch computations, but you should delete them before submitting your work.
     • Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that has been run.
     • To avoid experiencing Colab usage limits, save and close your notebooks once finished working.
4. Submit your implementation for Autograder feedback
   • Once you have implemented a portion of the required features, you may submit your work for feedback from the Autograder. To receive feedback, download your *.ipynb and *.py files then upload them to the Project 3 Autograder. You may submit to the Autograder for feedback up to 5 times per day.
5. Download final implementation
   • After implementing all features, save your work and download the completed *.ipynb and *.py files.
   • The last cell of the two_stage_detector.ipynb notebook will generate a uniqueid_umid_P3.zip file. The zip file should include convolutional_networks.ipynb, convolutional_networks.py, two_stage_detector.ipynb, two_stage_detector.py, one_minute_deepconvnet.pth, overfit_deepconvnet.pth, and rcnn_detector.pt for this assignment.
6. Submit your python and notebook files for grading
   • Upload your files to the Autograder for grading consideration. Your highest score will be used for final grades.

Deadline

This project is due on Tuesday, March 14th at 11:59pm EST. We suggest starting as soon as possible.

Grading

This project will be graded by the Autograder. The project is worth a total of 90 points. You may submit to the Autograder for feedback up to 5 times per day.