Practical Deep Learning: Lesson 1: Is it a Hotdog?30 Apr 2023
The biggest change since I last took a course on Machine Learning is one of the key points of this course: the use of foundational models that you fine tune to get great results. In this lesson’s video we fine tune an image classifier to see if a picture has a bird in it.
While building my own model I attempted to get the classifier fine tuned to look at comic book covers and tell me what publisher it was from. I thought with the publishers mark on 100 issues from Marvel, DC, Dark Horse, and Image the classifier would be able to tell. The best I was able to do was about 30% error rate, a far cry from the 0% in the example models. I tried a few different ideas of how to improve:
- train with larger images. The notebook used in the video makes the training go faster by reducing the size of the image. As I write this I wonder if it is even possible to use larger images in a model that might have been trained on a fixed size.
- create a smaller image by getting the 4 corners of the cover into one image.
- clean the data so that all the covers in the dataset had a publisher mark on them.
Nothing moved the needle. I’m hoping something I learn later in the course will give me the insight I need to do better.
I took a second attempt with a simpler project. Of course I remembered that Silicon Valley episode with the hot dog detector, and make a hot dog vs hamburger classifier. It works great. The next lesson covers getting a model like that into production, so hang tight.