keras-io/Object-Detection-RetinaNet

Model description

Implementing RetinaNet: Focal Loss for Dense Object Detection.

This repo contains the model for the notebook Object Detection with RetinaNet

Here the model is tasked with localizing the objects present in an image, and at the same time, classifying them into different categories. In this, RetinaNet has been implemented, a popular single-stage detector, which is accurate and runs fast. 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.

Full credits go to Srihari Humbarwadi

References

• RetinaNet Paper
• Feature Pyramid Network Paper

Training and evaluation data

The dataset used here is a COCO2017 dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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

Model Plot

View Model Plot

Model Reproduced By Kavya Bisht