Update README.md

README.md

@@ -1,5 +1,14 @@
 # CROWD COUNTER
 
+## Contents
+- [People](#introduction)
+- [Nature](#nature)
+- [Objects](#objects)
+- [Places](#places)
+- [Symbols](#symbols)
+- [Uncategorized](#uncategorized)
+
+
 ### Introduction
 
 This repository contains the code of performing the task of implementing a people counter from an overhead video surveillance camera by using Transfer Learning.
@@ -13,25 +22,23 @@ This repository contains the code of performing the task of implementing a peopl
 <p align="center">
   <img src="https://github.com/darpan-jain/crowd-counter/blob/master/pretrained-results/result1.png" width="640px" height="480px"/></p>
 
-- Clearly, using the pre-trained models is not the way to go. So, ~~Pre-Trained Models~~ 
+- Clearly, using the pre-trained models is not the way to go. So, ~~Pre-Trained Models~~ :confused:
 
 
 ### Training your custom model
 
 A custom model had to trained for accurate implementation. The following steps were taken for the same:
 
-1. **Annotated training data** had to be prepared before being given to the model.
+1. **Annotated training data** had to be prepared before being given to the model fot training.
 1. ***LabelImg*** was used for this purpose, to draw bounding boxes around objects of interest in the training images.
 1. LabelImg gives output as **xml** files containing coordinates of each of the bounding boxes in an image and the associated label of the object.
 1. All the xml files were converted to a ***train.csv*** and then into a ***train.record*** format. TFRecord format is required by Tensorflow to perform training of a custom model.
 1. Similarly a ***val.record*** was created for validation data.
 1. The architecture of the model is based on the **Faster RCNN** algorithm, which is an efficient and popular object detection algorithm which uses deep convolutional networks.
-1. The model config file was modified for the purpose of this assignment.
-1. The last 90 neuron classification layer of the network was removed and replaced with a new layer that gives output for only one class i.e. person.
+1. The config file of the model was modified. The last 90 neuron classification layer of the network was removed and replaced with a new layer that gives output for only one class i.e. person.
 1. The **config file** for the same can be found in `./data/utils/faster_rcnn.config`
 1. After training the model, the checkpoint model is saved as `model.pb` file.
-1. This model can now be deployed and used for obtaining inferences
-1. The test images were the first 10 frames of the mall dataset.
+1. This model can now be deployed and used for obtaining inferences on crowd images.
 
 - The model can be found on this drive link: ​[Custom Model](https://drive.google.com/open?id=1IBgEyaASf10KUFTCbky9mtruUpyoqDWR)
 
@@ -42,9 +49,13 @@ Upon running `main.py`, the results are as shown below. (Refer `./results`)
 
 <p align="center">
   <img src="https://github.com/darpan-jain/crowd-counter/blob/master/results/result0003.jpg" width="640px" height="480px"/></p>
+
+<p align="center">
+  <img src="          " width="640px" height="480px"/></p>
 
 ***Note:*** Since the model was trained on only **30** annotated images, the accuracy can be significantly increased by using a larger dataset to build the model.
 
+
 ### Prerequisites
 All the required dependencies can be install by running the command `pip install -r requirements.txt`