I started working through the first Lectures and wanted to share my initial project. As a Subaru fan, I wanted to create a classifier that distinguishes between two of the models, Forester and XV, which happen to look somewhat alike (being that one is a cross-over and the other a compact SUV). This and the fact that there are several generations of the models make the task at least mildly interesting/non-trivial.
Here is a preview of the data:
1. Initial Learning Rate Estimation and Model Training
Note that at this point we are already doing quite well.
*2. Unfreeze and Continuously Check Progress/Re-estimate LR
Things are still going well (no overfitting!) so we will do more
Now we seem to have ended up with a good set of weights
So let’s see the confusion matrix … lol
3.Conclusion We were able to achieve an excellent solution thanks to transfer learning (using a ResNet50 architecture) and the incredible fastai library. Overall we had just 616 data points, which definitely contradicts the common belief that one needs a lot of data to successfully use Deep Learning in practice.
The full notebook and the data are available at: ClassifyingSubarus
I am looking forward to exploring further projects and getting more familiar with the inner workings of the library, thanks Jeremy and team for a great course and library!