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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


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

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Model Reproduced By Kavya Bisht
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Space using keras-io/Object-Detection-RetinaNet 1