Murder City Machine Guns: Find Deals and Discounts Here

Alright, let’s talk about this “murder city machine guns” thing. It sounds way cooler than it actually was, trust me.

So, it all started last weekend. I was bored outta my skull, scrolling through some random forums, and stumbled across this thread about building a, uh, “high-performance” object detection system. The guy posting was all about squeezing every last drop of speed out of his models. Said something about making his system “run faster than a speeding bullet,” which I thought was hilarious and also a challenge.

First things first, I needed a dataset. I didn’t have anything lying around that was particularly suited for “machine gun” detection (thankfully!), so I decided to go with something similar – firearms. Found a public dataset with a decent amount of images, maybe around 5000. Figured that was enough to get started. Downloaded that sucker and got to work.

Next up, the model. Now, I’m no expert, but I’ve messed around with YOLOv5 before, so I decided to stick with what I knew. Pulled down the YOLOv5 repo from GitHub, set up my environment (Python 3.8, PyTorch, the usual suspects), and started tweaking the config file. I didn’t change too much, just adjusted the number of classes to 1 (since I only had “firearm”), and messed with the anchor sizes a bit based on the dimensions of objects in my dataset. This part always feels like voodoo magic to me, honestly. Just gotta kinda guess and check.

Training time. This is where the real waiting begins. I fired up my GPU (a humble RTX 2070, nothin’ fancy), and let the model train overnight. Used a batch size of 16, and trained for 300 epochs. Watched the loss curve like a hawk, praying it would actually converge. There was a moment where I was legit worried it was just gonna plateau, but thankfully, it kept going down. Patience is key, man.

After the training was done, I had a shiny new file. Time to see if this thing actually worked. I grabbed a few test images from the dataset and ran them through the model. And… well, it kinda worked. It was detecting firearms, sure, but the accuracy wasn’t amazing. Lots of false positives, and sometimes it would miss obvious guns right in the middle of the frame. Not exactly “murder city machine gun” level performance.

So, I spent the next couple of hours trying to improve the results. First, I tried increasing the confidence threshold. This helped reduce the false positives, but also made the model miss even more real guns. Then, I tried some data augmentation techniques – flipping, rotating, and scaling the images. This helped a bit, but not as much as I hoped.

Finally, I realized the problem was likely the dataset itself. It was just too small and too varied. Some of the images were blurry, some were taken in bad lighting, and some just didn’t have clear shots of the guns. Garbage in, garbage out, as they say.

In the end, I didn’t quite reach my goal of “faster than a speeding bullet” detection. The model was okay, but not great. But hey, that’s the name of the game, right? You try something, it doesn’t quite work, and you learn something in the process. Plus, I got a sweet story out of it. Maybe next time, I’ll try a bigger dataset, or mess around with a different model architecture. Who knows? The possibilities are endless.