Update training.rst

Previous link to the Tensorflow 1 Detection Model Zoo, https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md, leads to a page that didn't exist anymore (Error 404). 
The new link to the Tensorflow 1 Detection Model Zoo should be: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md.

Author: karan-1700

docs/source/training.rst

@@ -544,11 +544,11 @@ Configuring a Training Pipeline
 
 For the purposes of this tutorial we will not be creating a training job from the scratch, but rather we will go through how to reuse one of the pre-trained models provided by TensorFlow. If you would like to train an entirely new model, you can have a look at `TensorFlow's tutorial <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md>`_.
 
-The model we shall be using in our examples is the ``ssd_inception_v2_coco`` model, since it provides a relatively good trade-off between performance and speed, however there are a number of other models you can use, all of which are listed in `TensorFlow's detection model zoo <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md>`_. More information about the detection performance, as well as reference times of execution, for each of the available pre-trained models can be found `here <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models>`_.
+The model we shall be using in our examples is the ``ssd_inception_v2_coco`` model, since it provides a relatively good trade-off between performance and speed, however there are a number of other models you can use, all of which are listed in `TensorFlow's detection model zoo <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md>`_. More information about the detection performance, as well as reference times of execution, for each of the available pre-trained models can be found `here <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md#coco-trained-models>`_.
 
 First of all, we need to get ourselves the sample pipeline configuration file for the specific model we wish to re-train. You can find the specific file for the model of your choice `here <https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs>`_. In our case, since we shall be using the ``ssd_inception_v2_coco`` model, we shall be downloading the corresponding `ssd_inception_v2_coco.config <https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_inception_v2_coco.config>`_ file. 
 
-Apart from the configuration file, we also need to download the latest pre-trained NN for the model we wish to use. This can be done by simply clicking on the name of the desired model in the tables found in `TensorFlow's detection model zoo <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models>`_. Clicking on the name of your model should initiate a download for a ``*.tar.gz`` file. 
+Apart from the configuration file, we also need to download the latest pre-trained NN for the model we wish to use. This can be done by simply clicking on the name of the desired model in the tables found in `TensorFlow's detection model zoo <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md#coco-trained-models>`_. Clicking on the name of your model should initiate a download for a ``*.tar.gz`` file. 
 
 Once the ``*.tar.gz`` file has been downloaded, open it using a decompression program of your choice (e.g. 7zip, WinZIP, etc.). Next, open the folder that you see when the compressed folder is opened (typically it will have the same name as the compressed folded, without the ``*.tar.gz`` extension), and extract it's contents inside the folder ``training_demo\pre-trained-model``.