
## 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>
{"library_name": "keras", "tags": ["ObjectDetection", "RetinaNet", "ResNet50", "ObjectClassification", "Feature Pyramid Network"], "pipeline_tag": "object-detection"}
keras tensorboard ObjectDetection RetinaNet ResNet50 ObjectClassification Feature Pyramid Network object-detection arxiv:1708.02002 arxiv:1612.03144 has_space region:us
TAGS
#keras #tensorboard #ObjectDetection #RetinaNet #ResNet50 #ObjectClassification #Feature Pyramid Network #object-detection #arxiv-1708.02002 #arxiv-1612.03144 #has_space #region-us 

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:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>

### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>
TAGS
#keras #tensorboard #ObjectDetection #RetinaNet #ResNet50 #ObjectClassification #Feature Pyramid Network #object-detection #arxiv-1708.02002 #arxiv-1612.03144 #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>
passage: TAGS
#keras #tensorboard #ObjectDetection #RetinaNet #ResNet50 #ObjectClassification #Feature Pyramid Network #object-detection #arxiv-1708.02002 #arxiv-1612.03144 #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>