Lesson 1 - Official topic

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 :frowning:

I’d appreciate if anyone can guide me on what sort of steps one can take to reduce chances of having false positives? Thanks

Hi sttaq I hope you are having marvelous day!

You are likely always to have some false positives and negatives unless your model is a 100% accurate and recognises every image sent to it.

Here are some links that discuss what you are experiencing. Your model is acting as expected based on the data it was trained on and the image sent.

One of Jeremy’s key points is making sure that, test data contains some of the same images, that a model will see in production.

Your model is behaving as expected, you can try some of the ideas in the above threads, multilabel classification and training your model with more images to help improve your model.

Hope this helps

Cheers mrfabulous1 :smiley: :smiley:

Thanks @mrfabulous1, I’ll go through the links.

Hi – I’m seeing the “CLICK ME” “first_training” cell take a very long time (>15min for first epoch) to train/fine_tune on Google Colab, even with a GPU instance. Is this expected?

I’m having the same problem now. This does not seem to be normal. I ran the same code on an AWS GPU instance and it took only a minute. There seems to be a problem with Colab.