updated gernerating training data section in readme

Author: TannerGilbert

Diff for: README.md

@@ -125,35 +125,13 @@ LabelImg supports two formats, PascalVOC and Yolo. For this tutorial, make sure
 
 ### 4. Generating Training data
 
-With the images labeled, we need to create TFRecords that can be served as input data for training the object detector. To create the TFRecords, we will use two scripts from [Dat Tran’s raccoon detector](https://github.com/datitran/raccoon_dataset). Namely, the xml_to_csv.py and generate_tfrecord.py files.
-
-After downloading both scripts, we first change the main method in the xml_to_csv file to transform the created xml files to csv correctly.
-
-```python
-# Old:
-def main():
-    image_path = os.path.join(os.getcwd(), 'annotations')
-    xml_df = xml_to_csv(image_path)
-    xml_df.to_csv('raccoon_labels.csv', index=None)
-    print('Successfully converted xml to csv.')
-# New:
-def main():
-    for folder in ['train', 'test']:
-        image_path = os.path.join(os.getcwd(), ('images/' + folder))
-        xml_df = xml_to_csv(image_path)
-        xml_df.to_csv(('images/'+folder+'_labels.csv'), index=None)
-	print('Successfully converted xml to csv.')
-```
-
-Now we can transform our xml files to csvs by opening the command line and typing:
+With the images labeled, we need to create TFRecords that can be served as input data for training the object detector. To create the TFRecords, we will first convert the XML label files created with LabelImg to one CSV file using the [xml_to_csv.py script](xml_to_csv.py). 
 
 ```bash
 python xml_to_csv.py
 ```
 
-The above command creates two files in the images directory. One called test_labels.csv and another one called train_labels.csv.
-
-Next, open the generate_tfrecord.py file and replace the labelmap inside the class_text_to_int method with your own label map.
+The above command creates two files in the images directory. One is called test_labels.csv, and another one is called train_labels.csv. Next, we'll convert the CSV files into TFRecords files. For this, open the [generate_tfrecord.py file](generate_tfrecord.py) and replace the labelmap inside the class_text_to_int method with your own label map.
 
 Old:
 ```python
@@ -184,7 +162,7 @@ def class_text_to_int(row_label):
         return None
 ```
 
-Now the TFRecords can be generated by typing:
+Now the TFRecord files can be generated by typing:
 
 ```bash
 python generate_tfrecord.py --csv_input=images/train_labels.csv --image_dir=images/train --output_path=train.record

Diff for: generate_tfrecord.py

@@ -1,3 +1,5 @@
+#based on https://github.com/datitran/raccoon_dataset/blob/master/generate_tfrecord.py
+
 from __future__ import division
 from __future__ import print_function
 from __future__ import absolute_import

Diff for: xml_to_csv.py

@@ -1,3 +1,5 @@
+# based on https://github.com/datitran/raccoon_dataset/blob/master/xml_to_csv.py
+
 import os
 import glob
 import pandas as pd