Alright, let’s talk about this “keeneland images” thing. So, the other day I was messing around with some image data, and I stumbled across the Keeneland dataset – you know, horse racing stuff. Thought it would be a fun little project to play with. Here’s how it all went down.
First thing’s first: Getting the data. I wasn’t sure exactly where to grab it, but a quick search pointed me to a few different places. Ended up finding a decent chunk of images on some random racing websites and a few image search results. It wasn’t a neatly packaged dataset, so I had to do some manual downloading and sorting. A bit tedious, but hey, gotta start somewhere.
Next up: Cleaning house. This is always the most annoying part, right? I had a bunch of images of varying sizes and formats. Some were jpegs, some were pngs, some were weird proprietary formats I’d never seen before. So, I fired up ImageMagick – that thing is a lifesaver – and wrote a little script to batch convert everything to jpegs and resize them to a consistent resolution. Something like 512×512, just to keep things manageable. Used a simple bash loop for that, nothing fancy.
Labeling time. Ugh. This is where things got really manual. I wanted to try and categorize the images, maybe by horse, by jockey, by race type… something. So I just started going through them one by one, creating folders and dragging the images into the appropriate place. It was slow, but it forced me to actually look at the images, which turned out to be helpful later. I ended up with broad categories like “Horses,” “Jockeys,” “Crowd,” and “Finish Line.” Super basic, but good enough for what I had in mind.
Now for the fun part: playing with it. I wanted to see if I could train a simple image classifier to tell the difference between a “Horse” and a “Jockey.” I know, super basic, but I wanted to keep it simple. I tossed the data into a quick and dirty PyTorch script. Used a pre-trained ResNet model, did a little bit of fine-tuning, and let it run. Accuracy wasn’t amazing, but I was surprised it worked as well as it did, considering the mess of data I threw at it.
Visualizing Results: I then displayed some of the images with labels predicted by the model to see where the model had gone wrong. It seems it was having a hard time telling the difference between jockeys and horses up close. LOL!
Wrap up and thoughts: It wasn’t perfect, but it was a fun way to kill an afternoon. I learned a bit about image processing, a bit about PyTorch, and a bit about horse racing (sort of). Plus, I got to play around with some interesting data. Next time, I might try a different model, or maybe try to scrape more data to improve the results. But for now, I’m calling it a win. Maybe you guys can try it out and let me know what you come up with. Cheers!
