Add Google Colab Tutorial

2_Training/Train_YOLO.py

@@ -6,7 +6,7 @@ Retrain the YOLO model for your own dataset.
 import os
 import sys
 import argparse
-
+import warnings
 
 def get_parent_dir(n=1):
     """ returns the n-th parent dicrectory of the current
@@ -43,6 +43,7 @@ from keras_yolo3.yolo3.model import (
 from keras_yolo3.yolo3.utils import get_random_data
 from PIL import Image
 from time import time
+import tensorflow.compat.v1 as tf
 import pickle
 
 from Train_Utils import (
@@ -136,9 +137,20 @@ if __name__ == "__main__":
         default=51,
         help="Number of epochs for training last layers and number of epochs for fine-tuning layers. Default is 51.",
     )
+    parser.add_argument(
+        "--warnings",
+        default=False,
+        action="store_true",
+        help="Display warning messages. Default is False.",
+    )
 
     FLAGS = parser.parse_args()
-
+    
+    if not FLAGS.warnings:
+        tf.logging.set_verbosity(tf.logging.ERROR)
+        os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
+        warnings.filterwarnings("ignore")
+        
     np.random.seed(FLAGS.random_seed)
 
     log_dir = FLAGS.log_dir

README.md

@@ -24,6 +24,9 @@ To build and test your YOLO object detection algorithm follow the below steps:
 
 ## Getting Started
 
+### NEW: Google Colab Tutorial <a href="https://colab.research.google.com/github/AntonMu/TrainYourOwnYOLO/blob/master/TrainYourOwnYOLO.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+With Google Colab you can skip most of the set up steps and start training your own model right away. 
+
 ### Requisites
 The only hard requirement is a running version of python 3.6 or 3.7. To install python 3.7 go to 
 - [python.org/downloads](https://www.python.org/downloads/release/python-376/) 

requirements.txt

@@ -48,7 +48,7 @@ pandocfilters==1.4.2
 parso==0.5.0
 pexpect==4.7.0
 pickleshare==0.7.5
-Pillow==6.2.1
+Pillow==6.2.2
 prometheus-client==0.7.1
 prompt-toolkit==2.0.9
 protobuf==3.8.0