Lesson 1 - Official topic

[mrfabulous1]

Hi sttaq I hope you are having marvelous day!

@sttaq

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

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.

Handle data that belongs to classes not seen in training or testing

Let’s say I have trained an image recogniser to classify the content of an image as either a dog, a cat, a rat or an elephant. I put my model into production and it is for some reason fed with images of humans. The probabilities from the model suggests that there is 70% probability that an image of a human is an image of an elephant. I would rather like the model to say the input is not like any of the classes it was trained on. What is the go-to-way of handling this problem? I don’t have resou…

[Still Open] How to I tell my model to be honest and say "I don't know"

I have trained a model to detect tennis ball vs cricket ball. (Part1-Lecture 2)… now if I show it a picture of lets say a dog, I would like it to tell me … “I don’t know” instead of picking the “tallest amongst the midgets”.

First classifier -- Cat versus a Dog, a Monkey, and a House

Hi irl hope all is well! I agree with @mckennabrown points, and would add that the model hasn’t really learned to recognise cats and dogs but the relationship between the images and their labels of cats and dogs. This means that if you pass it any image it uses the relationship/model to calculate which class the picture fits best. This makes an interesting game one can play, which is before processing the image which the model is not trained for, you can try to guess what the classifier will …

Do i really look like a monkey: Unexpected image classification results?

Hi TauvicR Hope all is well! The fastai book covers this subject in some detail. In addition to the posts above I would look at the ethics notebook https://github.com/fastai/fastbook/blob/master/03_ethics.ipynb where you can see plenty of examples of your precise experience from major companies, and https://github.com/fastai/fastbook/blob/master/02_production.ipynb Jeremy gives an example of a skin classifier with unexpected consequences. Also in above notebook Jeremy talks about Avoiding d…

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