readme: ---
  library_name: keras
  tags:
  - ObjectDetection
  - RetinaNet
  - ResNet50
  - ObjectClassification
  - Feature Pyramid Network
  ---
  
  ## Model description
  
  Implementing RetinaNet: Focal Loss for Dense Object Detection.
  
  This repo contains the model for the notebook [**Object Detection with RetinaNet**](https://keras.io/examples/vision/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**](https://twitter.com/srihari_rh)
  
  ## References
  * [RetinaNet Paper](https://arxiv.org/abs/1708.02002)
  * [Feature Pyramid Network Paper](https://arxiv.org/abs/1612.03144)
  
  
  ## Training and evaluation data
  
  The dataset used here is a [COCO2017 dataset](https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip) 
  
  ## 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>
  
  ![Model Image](./model.png)
  
  </details>
  
  <center>
  Model Reproduced By <u><a href="https://github.com/robotjellyzone"><b>Kavya Bisht</b></a></u>
  </center>