Alright folks, lemme tell you about this thing I was messing with the other day – “black and models.” Sounds kinda weird, right? But trust me, it’s just what I named the folder where I was experimenting with some image stuff.
So, I started off just grabbing a bunch of images. I mean, a bunch. Scraped ’em from everywhere, basically. Some were stock photos, some were from random websites. The goal? To see if I could train a simple AI model to recognize, uh, well, different things in these pictures. I know, super vague, but that’s how most of my projects start – just a general idea and a whole lotta hoping.
First thing I did was clean up the data. You wouldn’t believe the junk I found. Images that were completely broken, duplicates, stuff that was just…not what I was looking for. Used a little Python script to weed out the bad ones. Honestly, data cleaning is like 80% of any AI project. Tedious, but crucial.
Then came the fun part: labeling. Ugh. This is where I wanted to give up, like, five times. Manually tagging thousands of images is soul-crushing. I was trying to get the model to recognize clothing types, facial expressions, and some other random stuff. Ended up using a labeling tool I found online. It made things a little easier, but it was still a grind.
Once the labeling was done (finally!), I started messing with different models. Tried a couple of pre-trained ones from TensorFlow and PyTorch. The pre-trained ones were okay, but not great for what I wanted. So, I decided to build my own simple CNN. It was nothing fancy – just a few convolutional layers, some max pooling, and a couple of fully connected layers at the end.
Training took a while, even on my decent GPU. Tweaked the hyperparameters a bunch – learning rate, batch size, optimizer. You know the drill. Watched the loss function like a hawk, trying to get it to go down. There were definitely some moments where I thought I’d screwed something up, but eventually, it started to converge.
After training, I tested it out on some new images. The results were…mixed. It was surprisingly good at some things, like identifying certain clothing types. But it completely failed on others, especially when the lighting was bad or the pose was unusual. I think I need a lot more data and a more complex model to get really good results.
Lessons learned? Data is king. The more data you have, and the better the quality, the better your model will be. Also, labeling sucks, but it’s gotta be done. And finally, don’t be afraid to experiment. Try different models, different hyperparameters, different everything. You never know what’s going to work.
So, yeah, that was my little “black and models” adventure. It was a fun learning experience, even though it didn’t produce anything groundbreaking. Maybe I’ll revisit it someday with more data and more time. Who knows?
- Cleaning data
- Labeling
- Model building
Summary
This project mainly focuses on a personal image processing project involving image data cleaning, labeling, model building and training. It recorded all the practice contents and summarized the practice experience.
