RetinaNet
RetinaNet es un avanzado arquitectura de aprendizaje profundo specifically designed for detección de objetos tasks in images. Introduced by Facebook Investigación en IA (FAIR) in 2017, it addresses the challenges faced by traditional object detection methods, particularly the desequilibrio de clases problem where the number of background examples greatly outnumbers the object examples.
La innovación clave de RetinaNet es su uso de una nueva función de pérdida llamada Pérdida Focal. Unlike standard funciones de pérdida 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 emplea una red de pirámide de características (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 detección de objetos en tiempo real sistemas.