Hello FastAI community,
I am new learner of ML and related stuff. I have been experimenting with Tensorflow and playing with teachable machine however, I was having trouble with a large number of false positives returned by my image recognition models. This led me to search for better ways and I ended up doing this course.
So, I started with Lesson 1, and thanks to Jeremy Howard excellent style of the book, I was able to train the cat vs dog model pretty quickly. However, when I pass this model images of babies having two pony tails they get categorized as cats with more than 90% confidence. I believe that the data set used in the book must be of very good quality compared to what I can collect on my own and if that data set is not good enough to train an accurate model than mine’s will never going to work
I’d appreciate if anyone can guide me on what sort of steps one can take to reduce chances of having false positives? Thanks