Okay, so yesterday I was messing around with some image recognition stuff, specifically trying to get something going related to the Nebraska Cornhuskers football team. Don’t ask me why, just had a wild hair.
First thing I did, obviously, was Google “Nebraska football images.” Started grabbing a bunch of pictures – players, the stadium (Memorial Stadium, of course!), the coach, the whole shebang. I made sure to get a variety of angles and lighting, figured that would help later on.
Next, I jumped into my usual Python setup. I’m using TensorFlow and Keras for this, pretty standard stuff. I started by setting up a basic image classification model. You know, convolutional layers, max pooling, the whole nine yards. I’m not going to bore you with the exact code, there are a million tutorials online, but it’s a pretty simple CNN.
The real pain in the butt was the data preparation. I had to manually label all the images. Like, “this is a player,” “this is the stadium,” “this is the coach.” That took forever. I tried using some pre-trained models to help speed things up, but they weren’t great at recognizing specifically Nebraska football stuff. Kept thinking everything was just generic “football.”
After labeling, I split the data into training and testing sets. I used like 80% for training and 20% for testing. Then I fed the training data into the model and let it run for a few epochs. I kept an eye on the accuracy, trying to tweak the model’s parameters to get it as high as possible.
The initial results weren’t great, I’m not gonna lie. The model was getting confused between the players and the stadium sometimes. So I went back and added more images to the training set, especially ones that were more clearly one or the other. That helped a little.
I also messed around with the learning rate and the optimizer. I started with Adam, but then I switched to SGD and played with the momentum. I think that actually made a bigger difference. The accuracy slowly started creeping up.
Finally, after a ton of tweaking and re-training, I got the model to a point where it was pretty decent. It wasn’t perfect, but it could correctly identify Nebraska football images most of the time. I even threw in a few images of Herbie Husker and it nailed those too!
Key Takeaways:
- Data labeling is a huge pain, but super important.
- Don’t be afraid to mess with the model parameters.
- More data is almost always better.
So yeah, that’s basically it. It was a fun little project. Maybe next time I’ll try to get it to recognize specific players or something. Go Big Red!
