Dramatic Data!
Table of Contents:
- 1. Deadline
- 2. Starter Package
- 3. Parts 1 to 2: The 474X Package
- 4. Part 3: The 595 Package
- 5. Submission Guidelines
- 6. Allowed and Disallowed functions
- 7. Collaboration Policy
- 8. Acknowledgements
1. Deadline
11:59:59 PM, Sept 12, 2024. Group Submissions (Groups of 3).
2. Starter Package
The Starter Package with the code and data can be downloaded from here. The starter code is in a Jupyter notebook with instructions.
3. Parts 1 and 2: The 474X Package
This part has to be completed for both the undergraduate and graduate version of the course. The details can be found in the introduction section of the .ipynb
file.
4. Part 3: The 595 Package
This part has to be completed for graduate version of the course along with parts 1 to 2. The details can be found in the introduction section of the .ipynb
file. Undergraduates can complete this part for a maximum of 20 extra credit (25%).
5. Submission Guidelines
If your submission does not comply with the following guidelines, you’ll be given ZERO credit. Please follow the submission guidelines provided in the .ipynb
file.
5.1. Report
For each part of the project, explain briefly what you did, and describe any interesting problems you encountered and/or solutions you implemented. You must include the following details in your writeup:
- Your report MUST be typeset in LaTeX in the IEEE Tran format provided to you in the
Draft
folder and should of a conference quality paper. Feel free to use any online tool to edit such as Overleaf or install LaTeX on your local machine. - Answer all the questions asked in the Jupyter notebook in the LaTeX report. Include outputs if asked/required to explain your answer. Try to always include outputs if applicable, these could be images (if output is an image) or numbers in some cases.
6. Allowed and Disallowed functions
Allowed:
- Any functions regarding reading, writing and displaying/plotting images in
cv2
,matplotlib
- Basic math utilities including convolution operations in
numpy
andmath
- Any functions for pretty plots
- Any functions for debugging, plotting (TensorBoard, Weights and Biases and so on) and metric implementation (confusion matrix, IoU)
- Usage of ChatGPT (or any other LLM) is allowed as long as you include the prompts used in your report and blatantly do not plagiarize from ChatGPT (includes copy pasting entire code)
Disallowed:
- Any function that implements in-part or full assignment including usage of LLMs such as ChatGPT to write code for this
If you have any doubts regarding allowed and disallowed functions, please drop a public post on Piazza.
7. Collaboration Policy
NOTE: You are STRONGLY encouraged to discuss the ideas with your peers. Treat the class as a big group/family and enjoy the learning experience.
However, the code should be your own, and should be the result of you exercising your own understanding of it. If you reference anyone else’s code in writing your project, you must properly cite it in your code (in comments) and your writeup. For the full honor code refer to the RBE474X/595-A01-SP Fall 2024 A-term website.
8. Acknowledgements
This project was partly inspired by Worcester Polytechnic Institute’s RBE595-F02-ST: Hands-On Autonomous Aerial Robotics course.