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
- Download the project starter code
- 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.
- Open the
*.ipynb
and*.py
files and implement featuresWe 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 theconvolutional_networks.ipynb
andtwo_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 thetwo_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.
- 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.
- 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
- 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 auniqueid_umid_P3.zip
file. The zip file should includeconvolutional_networks.ipynb
,convolutional_networks.py
,two_stage_detector.ipynb
,two_stage_detector.py
,one_minute_deepconvnet.pth
,overfit_deepconvnet.pth
, andrcnn_detector.pt
for this assignment.
- After implementing all features, save your work and download the completed
- 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 Friday, November 1st at 11:59pm CT. We suggest starting as soon as possible.
Grading
This project will be graded by the Autograder