RetinaNet Open-Source Object Detection Model - Fast and Accurate, Solve the Class Imbalance Problem!

Object Detection RetinaNet

Developed by keras-io

RetinaNet is an accurate and fast single-stage object detection model that uses a feature pyramid network and focal loss function to address class imbalance issues.

Object Detection #Dense Object Detection #Focal Loss Optimization #Multi-scale Feature Pyramid

Downloads 43

Release Time : 6/10/2022

Model Overview

This model is used to locate and classify objects in images. It adopts the RetinaNet architecture, combining a feature pyramid network and focal loss function to effectively handle multi-scale object detection and class imbalance problems.

Model Features

Single-stage Detector

As a single-stage detector, RetinaNet achieves fast detection while maintaining high accuracy.

Feature Pyramid Network

Uses a Feature Pyramid Network (FPN) to efficiently detect objects at different scales.

Focal Loss Function

Introduces the Focal Loss function to address foreground-background class imbalance issues.

Model Capabilities

Object Localization

Object Classification

Multi-scale Object Detection

Use Cases

Computer Vision

General Object Detection

Detects and classifies various objects in images

Performs well on the COCO dataset

Surveillance Systems

Real-time detection of people and objects in surveillance videos

name	learning_rate	decay	momentum	nesterov	training_precision
SGD	{'class_name': 'PiecewiseConstantDecay', 'config': {'boundaries': [125, 250, 500, 240000, 360000], 'values': [2.5e - 06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e - 05], 'name': None}}	0.0	0.8999999761581421	False	float32