Projects

List of projects that I am proud of

Clothes Classification using CNN

 

After created a Slipper Classifier using Logistic Regression, I wanted to use a more advanced Machine Learning method to train a more complex model. Expending from slippers, I planed to train a model to recognize different clothes. The database includes 6 categories: hat, pants, shoes, skirt, Tshirt and others.With a total number of 3293 Training images and 366 Validation images. The performance of this model is: Training Accuracy is 96.88%; Validation Accuracy is 88.36%. 

 

Blog link

GitHub link 

A Slipper Classifier using Logistic Regression

I had learned some Machine Learning knowledge, and would like to try to apply it on a real-world project. Slippers are commonly used in our daily life. I was thinking if there is a robot who can collect my slippers and put them in the shelf, I would buy it. So I chose slippers as my model training target. Logistic Regression was the training method in this project. The best Performance was: Training accuracy is 57%; Testing Accuracy is 53%.

 

Blog link

GitHub link

 

Passive Collision Warning APP

The title of my project is “A passive mobile collision warning system”. The aim of my project is to develop an iPhone application for driving safely. I named it "Watchahead". It can calculate the distance to the front car only according to a picture and give alarm if the distance is less than 25 meters.

Here is a blog of it.