2470 Project Reflection 3D Garbage Image Classification Team Garbage Pro: Hengguang Cui(hcui15), Ruichen Zhu(rzhu30), Runchang Zhou(rzhou32), Qiwen Li(qli97) 11/30/2022
Introduction: This can be copied from the proposal. We want to develop a model that could do 3D garbage image classification. In this project, we use 3D-MNIST dataset to imitate 3D garbage images. During the literature review, we see two main-streaming approaches for applying deep learning on 3D point clouds: one converting 3D objects to 2D and the other processing 3D directly. We found that the existing method of extracting features from 3D cloud points data has some limitations and would like to choose a Image-based method which converts the 3D objects to spheres with sphere projections and apply them with the Spherical CNNs. The main goal of this project is to project 3D images to spheres with Gaussian Mixture Model(GMM), and use a Spherical CNNs based model to do a 3D image classifications.
Challenges: What has been the hardest part of the project you’ve encountered so far?
None of us is familiar with S2CNN and it is not a well-known package. It takes a long time for us to understand it, and fix the module error caused by the package itself. The main challenge for the whole project is converting the 3D data to spheres with our Gaussian Mixture Model, and how to convert it to sphere is the hardest part. Currently, we are working on visualizing the 3D plots for the final posters and reports and getting a high accuracy with a better choice of layers for the model.
Insights: Are there any concrete results you can show at this point? How is your model performing compared with expectations? We have experimented 3d-based deep learning method using 3D CNN on the 3D MNIST dataset and achieved an accuracy of 65.5%. We have set up our spherical Gaussian Mixture model to converts datapoints in 3D dimension onto sphere surface and spherical CNN and not yet tested. We then Experimented using Random Forest and achieved an accuracy of 64.47%. We have also experimented using logistic regression and SVM on our task. We adapted the idea of One vs. Rest (OvR) to convert the multiclass-classification into binary classification. With 60000 images, the highest possible accuracy we can obtain is 35.56% for logistic regression and 40.33% for SVMs (how: To achieve that, we introduced a new algorithm that contains ten iterations, based on the number of classes, and we update the label of each observation to 1(yes) or 0 (no) based on the number we are observing) in each interation)
Plan: Are you on track with your project? What do you need to dedicate more time to? What are you thinking of changing, if anything?
Yes, we are on track with our project. We need to dedicate more time to tuning the parameters of Gaussian mean and standard deviation as well as the choice of layers. We also need to take care of the multi spheres with the same center and different radius. All of these are used to make our model become more accurate. And we are working on a way to make calculation of projection in GMM s reflect the importance of the data point in addition to attaining the depth variance. Most things work as expected, and nothing regarding our plan needs to change till now.
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