Devlog #14: building the dataset + training a tensorflow model

spent today on the data side of fire detection, which is half the battle with ML.

to train a model to recognize fire i need a solid labeled dataset, so first i built an image scraper. it crawls the web usinf breadth first search, going level by level through pages and pulling images as it goes. i used bidirectional bfs to expand the search from two fronts and cover more ground instead of just crawling one direction. ended up pulling a bunch of images, including a lot of tricky ones like trees and random orange stuff that a fire model could easily get confused by.

then i built a small labeling tool to actually tag all of it. it shows me one image at a time, a tree, an orange object, an actual fire, and i just mark it as fire or no fire. way faster than sorting through folders by hand, and getting those confusing orange non-fire images labeled is exactly what stops the model from false triggering later.

with some data labeled i started training a tensorflow model on it, mostly experimenting at this point to see how it behaves and what works. early days, but it’s a real step toward a model that actually learns what fire looks like instead of just matching colors.

good progress on the part of this project.