RetinaNet: Revision history

Diff selection: Mark the radio buttons of the revisions to compare and hit enter or the button at the bottom.
Legend: (cur) = difference with latest revision, (prev) = difference with preceding revision, m = minor edit.

16 December 2023

  • curprev 13:4313:43, 16 December 2023‎ Ai talk contribs‎ 3,846 bytes +3,846‎ Created page with "== Overview == RetinaNet is a focal loss based object detection model, designed to address the problem of class imbalance during training. It was introduced by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár in 2017. The model is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone network is responsible for computing a convolutional feature ma..."
Retrieved from "https://canonica.ai/page/RetinaNet"