writeup framework added

README.md

@@ -6,21 +6,34 @@
 <div style="text-align:center"><img src=./assets/Udacity_Header.png width="1000" height="400" ></div>
 
 
-# * Lyft Perception Challenge *
+# Lyft Perception Challenge
 
 
 <!-- ## About the challenge -->
-The [lyft Perception challenge](https://www.udacity.com/lyft-challenge) in association with Udacity had an image segmentation task where the candidates had to submit their algorithm which could segment road and cars pixels as precisely as possible. The challenge started on May 1st,2018 and went through June 3rd, 2018.
+The [lyft Perception challenge](https://www.udacity.com/lyft-challenge) in association with Udacity had an image segmentation task where the candidates had to submit their algorithm which could segment road and cars pixels precisely in real time. The challenge started on *May 1st,2018* and went through *June 3rd, 2018*.
 
 ## Approach
-Although it was a segmentation problem and did not require instance segmentation, I went ahead with [MASK-RCNN](https://arxiv.org/pdf/1703.06870.pdf) as it was the state of the art algorithm in image segmentation and I was always intrigued to learn about it.
+Although it was a segmentation problem and did not require instance segmentation, I went ahead with [MASK-RCNN](https://arxiv.org/pdf/1703.06870.pdf) as it was the state of the art algorithm in image segmentation and I was always intrigued to learn about it. Also I started on *28th*, just after finishing my first term, so transfer learning was my only shot. :sweat:
+
+#### Mask-RCNN
+Mask-RCNN, also known as [Detectron](https://github.com/facebookresearch/Detectron) is a research platform for object detection developed by facebookresearch. It is mainly a modification of Faster RCNN with a segmentation branch parallel to class predictor and bounding box regressor. 
+
 
-### Mask-RCNN
-Mask-RCNN, also known as [Detectron](https://github.com/facebookresearch/Detectron) is a research platform for object detection developed by facebookresearch. It uses Resnet as the backbone, 
 For this application Resnet-50 was used by setting `BACKBONE = "resnet50"` in config.
 
 ## Training
 
+#### MaskRCNN Configuration
+
+#### Processing Data
+
+#### Data Augmentation
+As the samples provided were very less (1K), data augmentation was necessary to avoid overfitting. [imgaug](https://imgaug.readthedocs.io/en/latest/) is a python module which came handy in adding augmentation to the dataset
+
+#### Training Loss
+
+Instead of a single training loop, it was trained multiple times in smaller epochs to observe change in the loss with changes in parameters and to avoid overfitting. As the data was less, the network used to saturate quickly and required more augmentations to proceed. Also i did not wanted to go overboard on the augmentation so was observing which one works best. Below are the logs of final training setting with the above given augmentation.   
+
 | heads Epoch | all Epoch | loss 							| val_loss 								|
 |:-----------:|:---------:|:-------------------------------:|:-------------------------------------:|
 | 10 		  | 40		  | ![loss](./assets/loss_40.png) 	| ![val_loss](./assets/val_loss_40.png)	|
@@ -40,7 +53,7 @@ Car F score: 0.519 | Car Precision: 0.509 | Car Recall: 0.521 | Road F score: 0.
 
 ## Inference and Submission
 
-## Submission
+### Submission
 Submission requires files to be encoded in a json. `test_inference.py` contains the inference and submission code. In attempt to increase the FPS, The encode function was replaced with the follows which was shared on the forum
 ```python
 def encode(array):