[1708.02002] Focal Loss for Dense Object Detection - arXiv.org
Aug 7, 2017 · To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is …
RetinaNet — Torchvision main documentation
The RetinaNet model is based on the Focal Loss for Dense Object Detection paper. The detection module is in Beta stage, and backward compatibility is not guaranteed. The following model …
GitHub - yhenon/pytorch-retinanet: Pytorch implementation of RetinaNet …
Pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár. …
RetinaNet Model for object detection explanation
Facebook AI research (FAIR ) team has introduced RetinaNet model with aim to tackle dense and small objects detection problem. For this reason, it has become a popular object detection …
Object Detection with RetinaNet - Keras
May 17, 2020 · RetinaNet uses a feature pyramid network to efficiently detect objects at multiple scales and introduces a new loss, the Focal loss function, to alleviate the problem of the …
How RetinaNet works? | ArcGIS API for Python | Esri Developer
RetinaNet is one of the best one-stage object detection models that has proven to work well with dense and small scale objects. For this reason, it has become a popular object detection …
RetinaNet: Single-Stage Object Detector with Accuracy Focus - Viso
Apr 17, 2024 · The RetinaNet model is a one-stage object detection model incorporating features such as Focal Loss, a Feature Pyramid Network (FPN), and various architectural …
RetinaNet: The beauty of Focal Loss | Towards Data Science
May 12, 2021 · The multi-task loss function in RetinaNet is made up of the modified focal loss for classification and a smooth L1 loss calculated upon 4×A channelled vector yielded by the …
RetinaNet - Papers With Code
Feb 19, 2021 · RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross …
Focal Loss for Dense Object Detection - IEEE Xplore
Oct 29, 2017 · To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is …