Alright, so I’ve been messing around with this thing called Fritz Prediction, and let me tell you, it’s been a bit of a rollercoaster. I wanted to build something that could, you know, predict stuff. Don’t ask me what exactly, I just like the idea of apps being smart.
First, I stumbled upon Fritz AI’s website. The whole thing looked pretty slick, all these promises of “easy machine learning.” I’m no data scientist, so “easy” sounded pretty darn good to me.
I signed up for an account—free to start, which is always a plus. Then I started digging through their documentation. Honestly, it was a bit overwhelming at first. There were so many options: pre-trained models, custom models, datasets, deployments… My head was spinning.
Getting Started (and Getting Stuck)
I decided to start simple. I picked one of their pre-trained models—I think it was image classification. The idea was to feed it a picture and have it tell me what’s in the picture. Sounds cool, right?
So, I followed their tutorial. They have these code snippets you can copy and paste, which is handy. I created a new project in my code editor, copy the code, and…BAM. error.
Turns out, I needed to install some stuff first. You know, libraries, dependencies, all that fun stuff. I spent a good hour just figuring out how to get the basic setup working. It involved a lot of Googling and some choice words directed at my computer screen.
Making Some Progress
Eventually, I got the basic example running. I uploaded a picture of my cat (because, of course), and the app correctly identified it as a cat! I felt like a genius for about five minutes.
Then I tried a picture of my dog. It said… cat again. Okay, so maybe it wasn’t perfect.
I realized that pre-trained models are only as good as the data they were trained on. My dog, apparently, looks a lot like a cat to this particular model. Fair enough.
The Custom Model Rabbit Hole
That’s when I decided to dive into custom models. This is where things got really complicated. I had to collect a bunch of images, label them (like, “this is a dog,” “this is a cat,” “this is a very confused-looking pigeon”), and then train the model.
The training process took forever. I let it run overnight, and when I woke up, my computer sounded like it was about to take off. But hey, it worked! Sort of. My new model was definitely better at telling the difference between my dog and my cat, but it still wasn’t perfect.
My conclusion
So, it still need a lot of time to working on it. I think that I can do it in the future, wish me luck!
