README.md

README.md

@@ -10,15 +10,22 @@ tags:
 
 ## Model description
 
-More information needed
+Implementing RetinaNet: Focal Loss for Dense Object Detection.
 
-## Intended uses & limitations
+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)
 
-More information needed
 
 ## Training and evaluation data
 
-More information needed
+The dataset used here is a [COCO2017 dataset](https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip) 
 
 ## Training procedure
 
@@ -37,4 +44,8 @@ The following hyperparameters were used during training:
 
 ![Model Image](./model.png)
 
-</details>
+</details>
+
+<center>
+Model Reproduced By <u><a href="https://github.com/robotjellyzone"><b>Kavya Bisht</b></a></u>
+</center>