Alright, so today I’m gonna share my experience tackling something I found pretty interesting: troy lesnar. Yeah, the wrestler. Don’t ask.
It all started when I was messing around with some image recognition stuff. I thought, “Hey, it would be cool to train a model to recognize faces. And why not start with someone super recognizable?” Troy Lesnar popped into my head, probably because I saw him on TV recently. Seemed like a good, beefy starting point.
First things first, I needed data. Lots of it. I went scraping on Google Images, DuckDuckGo, all the usual suspects. I used some basic Python scripts with Beautiful Soup and Requests to pull down a bunch of images of him. This took a while, tweaking the search terms and filtering out irrelevant stuff. Got a good chunk of photos – different angles, lighting, ages, you name it. Probably around 500 or so decent pics.
Next, the fun part: cleaning the data. Ugh. This is always the most tedious part. Had to manually go through each image and make sure it was actually him. You wouldn’t believe how many pictures of other bald, muscular dudes end up in a “troy lesnar” search. I also cropped the images to focus on his face and resized them to a consistent size. Used OpenCV for all this – super handy library.
Okay, now we’re ready to train a model. I went with a pretty standard Convolutional Neural Network (CNN) architecture, using TensorFlow and Keras. Nothing too fancy. A few convolutional layers, max pooling layers, and a fully connected layer at the end. I split the data into training and validation sets. The training part took a while, even with a decent GPU. I played around with different hyperparameters – learning rate, batch size, number of epochs – to try and get the best accuracy.
After a bunch of tweaking and waiting, I finally got a model that seemed to be performing reasonably well. It wasn’t perfect, but it could correctly identify Troy Lesnar in most of the test images. I even tried it out on some live webcam footage, and it worked surprisingly well! Pretty cool to see it in action.
So, what did I learn? Well, for one, data cleaning is a huge pain, but it’s absolutely crucial. Garbage in, garbage out, right? Also, even with a relatively simple model, you can get pretty decent results with enough data and some careful tuning. It was a fun little project, and I might try it with other wrestlers or celebrities next. Who knows?
- Scraped images using Python (Beautiful Soup, Requests)
- Cleaned and preprocessed data with OpenCV
- Trained a CNN model with TensorFlow and Keras
- Evaluated performance on a validation set
- Tested it out on live webcam footage
That’s pretty much it. Just a quick and dirty project to play around with image recognition. Maybe it’ll inspire you to try something similar. Go for it!
