Okay, so I got this new task today, building a simple image search with a touch of that fancy AI stuff. The project’s called “o archon have i done right”. Sounds cool, right? Let me walk you through what I did.
Getting Started
First things first, I set up my environment. I’m using Python for this because, well, it’s just super handy for these kinds of projects. I made sure I had all the necessary libraries installed – stuff like TensorFlow and Keras for the AI bit, and PIL for handling images. It was a bit of a hassle getting everything to play nice, but after a few tries, we were good to go.
Building the Model
Next up, I started working on the actual model. I decided to go with a pre-trained model called ResNet50. I mean, why reinvent the wheel, right? This thing is already pretty good at recognizing images. I just needed to fine-tune it a bit for my specific needs. I loaded the model and added a few extra layers at the end to help it understand the kind of images I’m interested in.
Training the Thing
Training the model was… an experience. I gathered a bunch of images, making sure I had a good mix of everything. I fed these images into the model and let it do its thing. It took a while, and my computer was definitely working overtime, but eventually, it started to get the hang of it. I kept an eye on the accuracy and loss metrics, tweaking things here and there until I was happy with the results.
Setting Up the Search
With the model trained, I moved on to the search part. I wrote a script that takes an image as input, runs it through the model, and finds similar images in my database. I used this thing called “cosine similarity” to compare images, which basically checks how close two images are in terms of their features. Sounds complicated, but it’s actually pretty neat.
Testing It Out
Finally, it was time to test everything out. I threw some random images at it, and honestly, I was pretty impressed. It was picking up similar images like a champ. There were a few misses here and there, but overall, it was working pretty darn well. I even showed it to a few friends, and they were like, “Whoa, that’s cool!”
Wrapping Up
So yeah, that’s pretty much it. It was a fun little project, and I learned a lot along the way. There’s still room for improvement, of course. I might try adding more data, or maybe experiment with different models. But for now, I’m pretty happy with how it turned out. “o archon have i done right”? I think so, I definitely think so.
- Set up the environment with Python.
- Installed necessary libraries like TensorFlow, Keras, and PIL.
- Chose and loaded the pre-trained ResNet50 model.
- Added extra layers for specific image recognition needs.
- Gathered a diverse set of images for training.
- Trained the model, monitoring accuracy and loss.
- Wrote a script for image input and search.
- Implemented cosine similarity for image comparison.
- Tested the system with various images.
- Showed it to friends and got positive feedback.
It’s been a ride, but a good one. And hey, isn’t that what coding is all about?
