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 able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.

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 able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.

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. This implementation is primarily designed to be easy to read and simple to modify.

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 entropy loss in order to focus learning on hard negative examples.

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 extreme foreground-background class imbalance.

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 builders can be used to instantiate a RetinaNet model, with or without pre-trained weights.

What is RetinaNet Model: – 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 model that one can use with aerial and satellite imagery also.

To evaluate the effective-ness 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 able to match the speed of pre-vious one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at:

Mar 4, 2025 · In this tutorial, you’ll learn how to fine-tune RetinaNet using PyTorch for accurate wildlife animal detection, achieving an impressive mean Average Precision (mAP) of 79% on a challenging wildlife dataset. RetinaNet’s unique architecture, featuring Focal Loss and a Feature Pyramid Network, effectively handles common problems like class imbalance and small or obscured animals—common ...

Feb 19, 2021 · RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-self convolutional network.