An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

Last update: Nov 30, 2021

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Overview

SVM

Données

Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests.

Prétraitements

Chaque image doit passer par la séquence des traitements suivantes.

Conversion l’image au niveau de gris
Binarisation (Noir et blanc)
Redimensionnement (120*80)
Conversion la matrice de l’image au vecteur
Insertion se vecteur dans une matrice (images)
Insertion dans un autre vecteur le nom de l’objet (Y).

Le Classifier utilisé

J’ai utilisé un classifier SVM de la bibliothèque sklearn, instancier avec une noyaux linaire et un paramètre de régularisation égal à 1.2,

Les résultats

Le temps d'exécution pour l’apprentissage sur 490 images est égal à 0.9839940071105957. Le taux de reconnaissance sur la base d'Apprentissage est 100%. Le taux de reconnaissance sur la base de Test est 100%.

Les tests

Owner

Achraf Rahouti

GitHub Repository

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