A neutral network to classify colors based on their RGB value.
The project was completed in 3 parts.
The data set was to have 3 freatures {r, g, b} and 9 classes including red-ish blue-ish green-ish and so on.
One data point consists of 3 integers (between 0-255) and a label. A random data sample from the data set is givin below:
{r = 133, g = 103, b = 152, label = 'purple-ish'}
I built a website to crowd-source my training data and collected 5000-ish data points.
As the data was crowd sourced, it needed to be cleaned to remove incorrect data points. I used Shiffman's implementation and cleaned my data using p5.
This project was supposed to be my entry point in the world of ML so I chose a rather high level and beginner friendly AI library - Scikit Learn. It gave me the tools to easily train my model while giving me the independence to play with all kinds of hyper-parameters. At the end the accuracy achieved was about 85%.
This was inspired by Daniel Shiffman who made a tutorial on the same project on his Youtube channel. I follow said tutorial for part of data collection and preprocessing. While both projects essentially do the same thing, it is important to note a few distinctions in our implementations.
| Shiffman... | I... |
|---|---|
| made model in JS | made model in Python |
| used Tensorflow | used Scikit Learn |
| used p5 to for data collection | used vanilla JS with jQuery sprinkled in |