The purpose of this app is to detect fire/smoke on forest images.

Web interface: https://mcdominik.github.io/forest_watchdog_front

Fullstack ML aplication with python+fastAPI. The model was trained and is working in PyTorch.

https://github.com/mcdominik/forest_watchdog

I used ResNet18 with pretrained weights and modified last layer to be binary (one neuron). I did simple 5 epoch training on kaggle dataset (link in the last paragraph). It was my first net in PyTorch and I had trouble with accuracy and loss measurements while training. I don't attach model charts because I basically don't know how model performs. I did some "manual" tests on new images and I predict pretty well.

The model has one big problem, when trees have red leaves (autumn photos), model predict it as fire. The solution should be to add some red-leaves pictures to training dataset and basic augumentation with some filters, different saturation etc.