Let's Stop Wildfires Hackathon

You can find more information on let's stop wildfires hackathon in the official repo.

I built a few notebooks in google colab. Feel free to use it, comment it or improve it!

1. Hackathon preparation

1️⃣ First challenge

The first task is to build a classifier to separate images with smoke and images without smoke.

👉 For the moment, I have around 0.142857 error_rate using a simple resnet30 after a lot of epochs.

2️⃣ Second challenge

The second task is almost the same: to build a classifier to separate images with smoke and images without smoke. Images are pieces of the images used in the first challenge.

👉 The classifier is faster to train. After a few epochs, the error_rate is around 0.066207.

3️⃣ Third challenge

Given a sequence of images, the task is to detect the fire as early as possible.

2. Hackathon

The goal of this hackathon is:

• decrease the number of false positive
• detect fire as early as posssible

We found out three ideas to try that take into account a sequence of 2 images as an output of a CNN.

First, we need to build a dataset. The hackathon organizers provided us a dataset of 23 sequences of images. Because this is not a lot of data, we are building a new dataset consisting of a combination of two images.

Rules used to build the new dataset:

• no_smoke(t) + no_smoke(t+1) = no_smoke
• no_smoke(t) + smoke(t+1) = smoke
• smoke(t) + smoke(t+1) = smoke
• no_smoke(t) + smoke_(t+2) = smoke

1️⃣ 2 images difference

To do list

• List all the images we have and label them in a csv file (mapping.csv) -- Oceane
• Write in a csv file all the combination of images to build our dataset (see above rules) -- Oceane
• Build our new dataset
• Write a function to calculate the difference of two image
• Write a script to split a picture into 81 pictures (for production)

2️⃣ 2 images stacking

3️⃣ CNN then neural network

Team