RetinaNet
RetinaNet est une architecture avancée d'apprentissage profond specifically designed for détection d'objets tasks in images. Introduced by Facebook Recherche en IA (FAIR) in 2017, it addresses the challenges faced by traditional object detection methods, particularly the déséquilibre des classes problem where the number of background examples greatly outnumbers the object examples.
L'innovation clé de RetinaNet est l'utilisation d'une nouvelle fonction de perte appelée Perte Focale. Unlike standard des fonctions de perte that treat all misclassifications equally, Focal Loss down-weights easy examples and focuses more on hard-to-classify instances. This helps the model learn better from the difficult cases, leading to improved accuracy, especially for detecting small or rare objects.
RetinaNet emploie un réseau pyramidal de caractéristiques (FPN) as its backbone, which allows it to extract features at multiple scales, enhancing its ability to detect objects of varying sizes. The architecture is a single-stage detector, meaning it processes images in one pass rather than requiring multiple stages like some two-stage detectors (e.g., Faster R-CNN). This design choice significantly increases the speed of detection while still maintaining competitive accuracy.
RetinaNet has been widely adopted in various applications, including autonomous driving, surveillance, and image analysis due to its robustness and efficiency. Its combination of speed and accuracy makes it a popular choice among researchers and developers working on la détection d'objets en temps réel systèmes.