Okay, so I stumbled upon this “ReID Shepard” thing, and I thought, “Why not give it a shot?” I mean, I’ve been messing around with person re-identification for a bit, and this seemed like a new angle to explore. So, I started by digging around for some info. Honestly, it wasn’t the easiest thing to find clear, step-by-step instructions.
First things first, I needed the code. I eventually found a repository that seemed legit. Cloned it to my machine, and started poking around. Gotta say, the file structure was a little intimidating at first. Lots of folders, lots of scripts. Made me feel like I was back in college, staring at a project I’d put off until the last minute.
Getting the Data Ready
Next up, data. This ReID stuff is hungry for data. I already had some datasets I’d used before, so I figured I’d start with those. The trick was getting them into the right format. This particular codebase had its own way of organizing things. You know, specific folder structures, naming conventions, the whole nine yards. This involved a bunch of tedious file renaming and moving stuff around. Not the most glamorous part of the process, but hey, gotta do what you gotta do.
I spent a good chunk of time just getting the data to play nice. It wasn’t super complicated, but it was definitely one of those things where if you mess up one tiny detail, the whole thing breaks. Been there, done that, got the T-shirt.
Tweaking and Training
With the data sorted, I moved on to the actual training. The code had a bunch of configuration files, filled with parameters to tweak. Batch size, learning rate, optimizer… the usual suspects. I started with the default settings, just to see if it would even run. Surprise, surprise, it didn’t. Error messages galore. Took some debugging, some Googling, and some good old-fashioned trial and error to get it going.
Once I got past the initial hurdles, I started experimenting. Changed the learning rate a bit, tried a different optimizer. Watched the training loss go up and down. It’s kind of like cooking – you gotta taste-test and adjust the spices until you get it just right.
- Tried different models: Swapped out the backbone a few times.
- Adjusted parameters: Played around with the learning rate, batch size, and other settings.
- Monitored performance: Kept a close eye on the training and validation metrics.
The Results (or Lack Thereof)
After a lot of fiddling, I finally got some results. Were they groundbreaking? Not really. Did it perform better than my previous attempts? Maybe a little. It’s hard to say for sure. This whole ReID thing can be pretty finicky. There are so many variables at play, it’s tough to isolate what’s actually making a difference.
Honestly, I’m still not 100% sure what I’m doing with this ReID Shepard stuff. It’s a learning process, I guess. I’ll probably keep messing around with it, see if I can squeeze out some more performance. But for now, it’s just another experiment in the ongoing saga of my machine learning adventures. Lots of up and downs, many experiments, and a little bit of the performance improved.
