Où est Charlie - AI

Hello, in this article we're going to find a technique to solve Où est Charlie with AI.

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I - Dataset

I found a dataset on the following github:
https://github.com/kiim29/Ou_est_charlie

The first step is to convert this data to Yolo v5 format.
To do this, I create the following directories:

yolov5/
├── train
│   ├── images
│   └── labels
└── val
    ├── images
    └── labels

And put all images in the good folder.

For the annotation, I create a script to read the csv in the github and create the various label files with the position type transformation.
Beacause the format of yolov5 labels file is :
index_item (xmin + xmax) / 2 / width (ymin + ymax) / 2 / height (xmax - xmin) / width (ymax - ymin) / height

import pandas as pd

class Main:


    def __init__(self):
        csv = pd.read_csv("../annotations/annotations.csv")
        for i in range(len(csv)):
            filename = csv["filename"][i].split(".")[0]
            width = csv["width"][i]
            height = csv["height"][i]
            xmin = csv["xmin"][i]
            ymin = csv["ymin"][i]
            xmax = csv["xmax"][i]
            ymax = csv["ymax"][i]

            x_center = (xmin + xmax) / 2 / width
            y_center = (ymin + ymax) / 2 / height
            bbox_width = (xmax - xmin) / width
            bbox_height = (ymax - ymin) / height


            with open(f"../dataset/train/labels/{filename}.txt", "a") as f:
                f.write(f"0 {x_center} {y_center} {bbox_width} {bbox_height}\n")






if __name__ == "__main__":
    Main()

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II - Train

For training I use ultralytics

pip install ultralytics

And I launch the training with the following arguments.

from ultralytics import YOLO

model = YOLO('yolov8n.pt')

model.train(data='dataset.yaml', epochs=50, patience=10000)

model.export()

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III - Test

For testing purposes, we can run a random image with ultralytics and the new model :)

import sys

from ultralytics import YOLO


model = YOLO('../last.pt')

image_path = f'../dataset/train/images/{sys.argv[1]}.jpg'

results = model(image_path,conf=0.2)