Alright, so lemme tell you about this little project I tackled recently: “tiger vs grizzly.” Sounds intense, right? Well, it was mostly me wrestling with code and some image datasets, not actual animals, thankfully.
First, I downloaded a bunch of images. I mean, a ton of images of tigers and grizzly bears. Used a mix of Google Images and some datasets I found on Kaggle. Gotta have variety, you know? Some close-ups, some far away shots, different angles, different lighting. The more, the merrier!
Next up, cleaned the data. This part is always a pain. Renaming files, getting rid of corrupt images (there were a few!), and organizing everything into two separate folders: “tigers” and “grizzlies.” Gotta keep things neat and tidy.
Then I fired up my trusty Python environment. Needed to get my hands dirty with some image processing. Used OpenCV for resizing the images to a consistent size. Something like 224×224 pixels. And also did some preprocessing – normalizing the pixel values to be between 0 and 1. Standard stuff.
After that, it was time to build the model. Went with a simple Convolutional Neural Network (CNN) using TensorFlow/Keras. A few convolutional layers, some max pooling, and then a fully connected layer at the end with a sigmoid activation for the binary classification. Nothing too fancy, just wanted something that would work.
Split the data into training and validation sets. Like, 80% for training and 20% for validation. Gotta see how well the model generalizes, right? Then I trained the model. Let it run for a few epochs, keeping an eye on the accuracy and loss. Tweaked the learning rate a bit to get better results.
Once I was happy with the training, I tested it out on some new images. Images the model hadn’t seen before. Checked the predictions. Saw how well it was actually performing. It wasn’t perfect, but it was surprisingly good! Got around 85% accuracy. Not bad for a weekend project.
Finally, I saved the model. So I could use it later without having to retrain it every time. And then wrote a little script to take an image as input and output whether it’s a tiger or a grizzly. Just for fun.
It was a pretty straightforward image classification project. Learned a few things, got to play with some cool tools, and now I can (mostly) tell the difference between a tiger and a grizzly. Not bad for a weekend, eh?
