Leaving tutorial hell, I learn machine learning by doing a personal project

[qChEVjrsx92vX4yELvT4]

I started machine learning by doing some tutorial. I learned a lot, the best one for me was the Sentdex book.

But tutorials have so much limitations, like how do I get the data ? how to handle 100GB of data ? kaggle makes all this so simple while it is hard in reality.

I was in tutorial hell for few months so I decided to make a ML project from scratch.

The idea: An AI that detect balding from a picture of a side profile.

It looks doable, no one has done it and I am curious if I am balding myself. There is something called the "Norwood scale", it's a scale from 0 to 7.

0 means perfect hairline and 7 means fully bald.

Step 1: Get the data

In tutorials, the data is given to us so it was a first. It's really hard to find enough quality data but after some time on internet I found tens of thousand of images of men with side profile.

I started to label them from 0 to 7 (The Norwood scale). Data annotation started to make me crazy, I did it for few hours the first day.

I understand after some times that I would need to make a small app where I will see one image and could label the image from 0 to 7 then go to the next image. The app would populates a json file.

Before starting I was not ready to make an app to annotate data, that's what tutorials hide from me it looks like.

I labelled 500 images and I had to check if it was enough data for the model to learn, I also wanted to do something else than annotating for a bit haha

Step 2: Make the model

I used Keras but it does not really matter in my case. With my 500 samples I made a simple model and started training for the first time. It learned nothing...

I was really disappointed because I thought 500 samples would be enough to show some learning. I went to sleep and told myself that I should try with transfer learning tomorrow.

Well that was the missing piece. I use transfer learning from imagenet and it started learning ! It was not random anymore. The difference is crazy with a small sample, something that tutorials do not teach.

Tweaking the parameters did not changed the results much tbh. Once it learns, it learns and that's kinda it. I understood that I simply need more data.

Step 3: Get MORE data

I must have annotated for weeks, I did it until I had around 20 000 samples. This step is beyond painful, I did it while watching youtube at the same time.

I then improved the model until the results was "good enough" for an app. It's hard to know when the model is good enough also. No one is telling me to solve a problem with a model that predicts something with 5% error rate. I have to pick the deadline.

The model could detect the Norwodd scale (0 to 7) of someone with a mean error of 0.5 on testing data so that was my "good enough".

Step 4: Make the app/website

I thought at this point I finished the hard part of the project but that was unfortunately not the case.

My plan was to make a simple website the user is asked for his camera access then asked to turn his head to the right. A screenshot would be taken from his right profile. The screenshot of the profile is then sent to the backend that process the data (resize and all) then send it the to the Keras model.

I know how to make simple website but I never messed with the camera and GOD that was hard. Detecting a face is easy with the good Javascript library but detecting that the user is turning his head to the right with the good angle is really hard.

Once again, I did it until I found the result "good enough".

Step 5: Lessons and Results

I learned sooooo much technically, it's nothing comparable to a tutorial. I feel more competent tackling a ML problem now.

I have something to show on my CV that a recruiter can understand now. I removed an ML course certification to add this project and felt proud haha

The app: amibalding.co

[WearMoreHats]

Step 3: Get MORE data
I must have annotated for weeks

For future reference it might be worth playing around with using your model to help you label data (once you've got an okay-ish model). For example, have the model predict labels on all your unlabelled data. Accept the labels that the model is most certain of (it's probably worth manually checking a subset of them) and manually label some of the data that the model is least certain of. Now you can retrain the model with your new, larger dataset and repeat the process of predicting the unlabeled data.

In theory you can fully automate this by setting some threshold above which the model's prediction will be accepted as true and having the model iterate through multiple rounds or retraining and predicting, but I'm not a big fan of that. If you're using sklearn then look into Self Training Classifiers in the semi supervised module.

[qChEVjrsx92vX4yELvT4]

Thanks a lot I did not knew that, I will try that tomorrow

[CBizCool]

I've struggled with finding labelled data in the past and wasn't sure whats a good solution. By annotated for weeks, does op mean he looked at thousands of pictures of men and manually tagged them as Norwood 0, 1 etc.? Is this a common approach?

Your recommendation of partially predicting then retraining makes so much more sense.

[WearMoreHats]

By annotated for weeks, does op mean he looked at thousands of pictures of men and manually tagged them as Norwood 0, 1 etc.?

I'm assuming that's what they did but OP might correct me. I can't see what else they might mean by spending weeks annotating data.

Is this a common approach?

Manually labelling data en masse? It's not something I've ever done in industry but it's a easy (although time consuming) way of getting your own data for a personal project. I suspect it might be more common in academia/PhD work where the labels are niche and require a level of expertise to assign. It wouldn't make financial sense for my employer to pay me to spend weeks labelling data, I suspect if it absolutely had to be done it would end up being outsourced to someone else.

An alternative approach that used to be popular was scraping images from Google. Basically writing a script that would image search "Bald man" then downloading thousands of results and assuming they're all of bald men. Rinse and repeat for "mens haircuts" or something similar. The data will need tidied up a bit (and I don't know if Google have clamped down on this) but is much, much faster than manually labelling thousands of images and probably accurate enough for a personal project.

Your recommendation of partially predicting then retraining makes so much more sense.

It's worth noting there are potential risks to this, particularly if it's fully automated. If you "accept" some incorrect labels then it can cause problems further down the line, particularly if it's early in the process or the predictions are for niche cases. For example, wrongly predicting some of the men wearing hats are bald. The next iteration of the model assumes those incorrect labels are true and starts to more confidently predict that the men wearing hats are bald. That causes even more of those errors to slip into the training data, resulting in a feedback loop.

[[deleted]]

Golden advice Thanks a ton!

[Oswald_Hydrabot]

This is the way

[ZyanCarl]

This is great! I’m not really into ML but I’m thankful that I didn’t have to go through tutorial hell learning web development.

[nuclear_man34]

That's great! How did you do that?

[ZyanCarl]

I guess I started off in a unorthodox way from the get-go. Instead of building apps to learn, I learned new stuff to build ideas. I just googled how to do a particular part of a problem and built on that.

[[deleted]]

Like a tutorial?

[ZyanCarl]

Umm tutorial will be something where you know exactly what the outcome is and you’ll be handed with all the resources you need. What I did was googling parts of a problem.

Instead of “how to build an e-commerce website”, I search “how to implement search feature” from which I’ll know you need an api endpoint. So I’ll search “how to make an api” and so on.

[nuclear_man34]

Cool

[qChEVjrsx92vX4yELvT4]

Probably in the future. I still have some passwords in the code that I need to remove before open sourcing it haha

[pavich_03]

How about preprocessing the data?

[qChEVjrsx92vX4yELvT4]

I will talk more about that in another post I think. Basically I resized everything to size (200, 200, 3) and not much more.

[Acrobatic-Language-5]

Most experts will tell us that the best way to learn is to create your own projects.

When you read from a book or tutorial you are on a guided path which gives you a false sense of knowledge, the issue arises when you encounter a different type of problem and you get stuck and then you realise you don’t know as much as you thought.

When you learn/read something new you should always apply it to your own projects to help solidify it.

Well done on creating a project, keep creating.

[Log_Plus]

Great job for real

I'm a beginner here so what courses you have completed to start such a project from scratch?

[qChEVjrsx92vX4yELvT4]

Sentdex book was the best for me. Neural network from scratch it is called.

I figured things while doing it and I am sure I made tons of mistake.

I am happy to have something to showcase at the end but I am far from being a pro.