Machine learning for image understanding

Introduction
This mini-project is about using machine learning for image understanding. It will give you experience with working image data. You will be working on a 9-class classification problem.
Here, you will be working with a modified version of the Fashion-MNIST dataset constructed for this mini-project. In this dataset, each image contains three articles, and the goal is to output the total price of all articles presented in the image. Articles, from the least to the most expensive, are: T-shirt/top ($1), Trouser ($2), Pullover($3), Dress ($4), Coat($5); where the number within parenthesis shows the price associated to each article. Examples of this task are shown in the following figure. For example, the target label for the top-left image would be 5, while the target label for the bottom-right image would be 10.
 
Note that this is a supervised classification task: Every image has an associated label (i.e., total price) and your goal is to predict this label. You will see that in the provided dataset, the cheapest set (i.e. image) costs $5 and the most expensive set costs $13. Therefore, it is a nine-way classification problem.
Download the training and test data (see below for more details). Train.pkl includes 60,000 training images where the class label for each image is provided in the TrainLabels.csv file. Test.pkl includes 10,000 test images. You need to generate a .csv file that includes your predicted labels for the test images. Use TrainLabels.csv besides Train.pkl to train your models.
Your task
You must design and validate a supervised classification model to perform the Modified Fashion-MNIST prediction task. There are no restrictions on your model, except that it should be written in Python. As with the previous mini-projects, you must write a report about your approach, so you should develop a coherent validation pipeline and ideally provide justification/motivation for your design decisions. You are free to develop a single model or to use an ensemble; there are no hard restrictions.
You must run your model on the test data provided in Test.pkl and submit the result on Kaggle competition. (See below for more details)
Kaggle competition
Test.pkl contains test images. You need to make a prediction for all test images and submit a .csv file in Kaggle where each line contains the corresponding image index (between 0 and 9999) and predicted label which is an integer number between 5 and 13.
ExampleSubmissionRandom.csv is an example of the kind of .csv file you should submit in Kaggle. (Note: in the example file, the class label written in each line, in the second column, is just a random value and you should replace them with your predicted values). You are limited to two submissions per day.
You must register in the Kaggle competition using your mcgill.ca. If you already have a Kaggle account under a different email address, do not delete your account. You only need to change your email in your Kaggle profile. You must form a team on the competition page on Kaggle, the name of your team must be the same as the name of your group on myCourses (e.g. Team Name: Group 10). Except where explicitly noted, you are free to use any Python library for this project. You are not allowed to use any training data other than what is provided for the competition. To be clear, using the original fashion-mnist dataset is not allowed. Here is the link to the Kaggle competition.
Report
We are flexible on how you report your results, but you must adhere to the following structure:
●    Abstract (100-250 words): provide a summary of the project task and highlight your most important findings.
●    Introduction (at least one paragraph): Summarize the project task, the dataset and your important findings. This is similar to the abstract but you should provide more details.
●    Dataset (at least one paragraph): Briefly describe the dataset. Also, describe the preprocessing steps (if you have any) for preparing the input data.
●    Proposed Approach Briefly describe the methods you have implemented or used for this project. No need to provide detailed derivations and proofs but you need to provide some background, description and motivation for each model. You should properly cite and acknowledge previous works/publications that you use or build upon. Discuss any decision about training/validation splits, algorithm selection, regularization, hyper-parameters, etc. 
●    Results Summarize your results using tables and/or figures. Discuss the results for each model (for example accuracy, runtime, etc.). Since you do not have the labels for the test set, your results should be based on your validation set(s). Report your test set leaderboard accuracy too.  
●    Discussion and conclusion Discuss and summarize the key takeaways from the project and possible directions for future investigation.
●    Statement of Contributions (1-3 sentences) State the breakdown of the workload across the team members.
●    Appendix To facilitate the grading process, attach the codes for your implementations to the end of your report. This does not count towards the page limit of the report. You must also submit your code as a .zip file.
You are expected to discuss your findings with scientific rigour. you can discuss why did you get the results you did? Compare and contrast the different algorithms. What sort of changes might you make to each of those algorithms to improve performance? How fast were they in terms of wall-clock time? Iterations? Would cross-validation help? Which algorithm performed best? How do you define best? Be creative and think of as many questions you can, and as many answers as you can.
Please keep your analysis as short as possible while still covering the requirements of the assignment. The analysis report is limited to 5 pages (single-spaced, minimum font size of 10 and 1-inch minimum margin each side). We highly recommend using LaTeX for preparing your report. We recommend NeurIPS2020 LaTeX template. Your report should look technical. Imagine you are writing a paper for a major machine learning conference.
Deliverables
●    report.pdf: Your report (including Appendix) as a single pdf file. This must be submitted as a separate file.
●    code.zip: Your codes (e.g. .py, .ipynb, etc.) must work with Python 3.6 in Colab. Include a readme file and provide instruction for TA on how to replicate your results on Colab. All the results must be reproducible in Colab using the submitted code.zip. Points will be deducted if we have a hard time reading or understanding the structure of your code. Do not put the report.pdf in the zip file.
The report should be self-contained. TA's will do the grading mainly based on the report.pdf, and will not be obliged to consult the supplementary codes.
Evaluation
This is an open-ended project. Feel free to go beyond the minimal requirements. The evaluation has two parts each worth 50 points out of 100.
Performance (50 points): This is based on the performance of your best model on the held-out test set on the Kaggle competition. Your grade will be computed based on a linear interpolation between three points: the 2nd top group, a TA baseline and a random baseline. The random baseline is the score needed to get more than 0% on the competition. The TA baseline is the score needed to get 75% on the competition. In other words, if your score is between the random and TA baseline, your grade is a linear interpolation between 0% and 75% on the competition; likewise, if your score is between the TA baseline and the 2nd best group, your grade will be between 75% and 100% on the competition. In addition to the above criteria, the top two groups all receive 100%. Additionally, the top group will receive 10% points as a bonus.
Quality of your report and proposed methodology (50 points): Your report should be both thorough and concise. It will be judged based on its scientific quality including but not limited to: Does the report include all the required experiments? Is the report technically sound? How thorough/rigorous are your experiments? Is the report well-organized and coherent? Is the report clear and free of grammatical errors and typos? Does the report contain sufficient and appropriate references and related work? 
All members of a group will receive the same mark.
Final remarks
You are expected to display initiative, creativity, scientific rigour and critical thinking skills. You don't need to restrict yourself to the requirements listed above - feel free to go beyond, and explore further.
You can discuss methods and technical issues with members of other teams, but you cannot share any code or data publicly or with other teams. Any team found to cheat (e.g. use external information, use resources without proper references) on either the code, predictions or written report will receive a score of 0 for all components of the project. Sharing or posting the mini-project specifications, including your code, publicly, whether intentionally or unintentionally, is considered academic misconduct.