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keras-io/Object-Detection-RetinaNet

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

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

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

<details> <summary>View Model Plot</summary>

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</details>

<center> Model Reproduced By <u><a href="https://github.com/robotjellyzone"><b>Kavya Bisht</b></a></u> </center>