Sun Detection Using YOLOv8

Imagine teaching a computer to spot the sun in sunset pictures—well, that’s what this project is all about. In this project, we have a batch of 17 images of sunsets, and our mission is to train a special YOLOv8 model to recognize the sun.

First, we’ll mark these images, drawing little boxes around the sun so the YOLO model knows what it’s looking for. Then, we’ll split our collection into batches—one for training the model and another for testing.

Then, we’ll set up the YOLOv8 software and tinker with its settings. We will ensure it understands that we only want to find the sun in the pictures. This will be similar to teaching our smart machine friend a new skill.

Once everything’s set, we’ll train our YOLOv8 model using our custom sunset dataset while closely monitoring its performance. If it doesn’t perform well, we’ll give it more practice using extra epochs or fine-tuning its settings.

By the end of this project, we’ll have gained insights into how YOLOv8 works, become proficient at training models on our own custom sets of images, and learned how to tell if a computer’s efficient at spotting sunsets. We might face challenges like ensuring our annotations (the little boxes around the sun) are spot on, especially when the sun’s hiding behind clouds or trees. Moreover, training the computer will consume time and resources, so we’ll have to manage that too.