COMP551-Multi-Label-Classification Solved

1.   code.zip: Your data processing, classification and evaluation code (.py and .ipynb files).

2.   writeup.pdf: Your two-page report of the project.

•   We recommend using Overleaf for writing your report and Google colab for coding and running the experiments. The latter also gives access to the required computational resources. Both platforms enable remote collaborations. You may also choose to use the Google cloud credits provided to you to run the experiments.

•   This project also includes a Kaggle competition. It is important that you participate in it and follow the competition rules given below:

1.   Do not register on Kaggle unless you have finalized your group in MyCourses.

2.   Create a group on Kaggle, where the group name should be the same as the registered group in MyCourses.

3.   Only one Kaggle account is allowed per individual. Students must form a team before uploading their solution. Any violations will be noted and penalized appropriately.

4.   Privately sharing code or data outside the team is not allowed. It is okay to share small parts of code on Kaggle, if it is made available to all the participants in the class.

5.   Participants are not allowed to use data from outside. They should use the data provided to train their classifier. But you are allowed to perform any operations on the given data.

6.   Team mergers are not allowed in this competition.

7.   Each team may submit a maximum of 2 solutions per day.

8.   You may select up to 2 submissions as a final solution for private leaderboard ranking (the best one will be selected).

2          Problem Statement
In this mini-project, you will develop models to classify the image data. You will use the combo MNIST dataset (https://www.kaggle.com/c/comp-551-fall-2021/data) provided to you for this problem. Combo MNIST dataset has 60,000 images for training your classifier. Out of the 60,000 training images, 30,000 images have labels associated with them. The remaining 30,000 images do not have labels. You can get creative to use them to train your classifier. Sample images are shown in Figure 1 to give you an idea of the image dataset. The dataset also comes with 15,000 test images whose predictions has to be uploaded to Kaggle to get a score. Roughly 40% these 15,000 images are used to show the rankings in the public leaderboard, while the remaining 60% form private leaderboard which is hidden from everyone. The final rankings are based on the private leaderboard. Please read the evaluation page on Kaggle (https://www.kaggle.com/c/comp-551-fall-2021/overview/evaluation) carefully for formatting your submission file.

 

Figure 1: Sample images from combo MNIST dataset

Each image consists of two characters: one letter from the English alphabet (26 letters in total) and one digit from numbers 0-9 (10 digits in total). These characters can be of different size and may appear in any part of the image with different orientations. Additionally, these images may also be affected by noise. Your task is to train a model that can correctly identify the characters in the image. You are free to use any Python libraries you like to extract features, pre-process the data, evaluate your model, and to tune the hyper-parameters, etc.