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Update README.md: links and updated TF API code #329

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Update README.md: links and updated TF API code #329

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17 changes: 9 additions & 8 deletions README.md
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Expand Up @@ -43,8 +43,8 @@ TensorFlow-GPU allows your PC to use the video card to provide extra processing
## Steps
### 1. Install Anaconda, CUDA, and cuDNN
Follow [this YouTube video by Mark Jay](https://www.youtube.com/watch?v=RplXYjxgZbw), which shows the process for installing Anaconda, CUDA, and cuDNN. You do not need to actually install TensorFlow as shown in the video, because we will do that later in Step 2. The video is made for TensorFlow-GPU v1.4, so download and install the CUDA and cuDNN versions for the latest TensorFlow version, rather than CUDA v8.0 and cuDNN v6.0 as instructed in the video. The [TensorFlow website](https://www.tensorflow.org/install/gpu) indicates which versions of CUDA and cuDNN are needed for the latest version of TensorFlow.

If you are using an older version of TensorFlow, make sure you use the CUDA and cuDNN versions that are compatible with the TensorFlow version you are using. [Here](https://www.tensorflow.org/install/source#tested_build_configurations) is a table showing which version of TensorFlow requires which versions of CUDA and cuDNN.
<!--- The link below was showing results for Linux. Changed it to Windows table --->
If you are using an older version of TensorFlow, make sure you use the CUDA and cuDNN versions that are compatible with the TensorFlow version you are using. [Here](https://www.tensorflow.org/install/source_windows#gpu) is a table showing which version of TensorFlow requires which versions of CUDA and cuDNN.

Be sure to install [Anaconda](https://www.anaconda.com/distribution/#download-section) as instructed in the video, because the Anaconda virtual environment will be used for the rest of this tutorial. (Note: The current version of Anaconda uses Python 3.7, which is not officially supported by TensorFlow. However, when creating an Anaconda virtual environment during Step 2d of this tutorial, we will tell it to use Python 3.5.)

Expand Down Expand Up @@ -139,7 +139,8 @@ Install the other necessary packages by issuing the following commands:
(tensorflow1) C:\> pip install Cython
(tensorflow1) C:\> pip install contextlib2
(tensorflow1) C:\> pip install jupyter
(tensorflow1) C:\> pip install matplotlib
(tensorflow1) C:\> pip install

(tensorflow1) C:\> pip install pandas
(tensorflow1) C:\> pip install opencv-python
```
Expand Down Expand Up @@ -184,8 +185,8 @@ This opens the script in your default web browser and allows you to step through
(Note: part of the script downloads the ssd_mobilenet_v1 model from GitHub, which is about 74MB. This means it will take some time to complete the section, so be patient.)

Once you have stepped all the way through the script, you should see two labeled images at the bottom section the page. If you see this, then everything is working properly! If not, the bottom section will report any errors encountered. See the [Appendix](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#appendix-common-errors) for a list of errors I encountered while setting this up.

**Note: If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements on line 25 and 26 that include matplotlib. Then, try re-running the Jupyter notebook.**
<!--- In updated visualization_utils.py those are lines 29 and 30 now. I had this problem and it worked for me as well. --->
**Note: If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements on line 29 and 30 that include matplotlib. Then, try re-running the Jupyter notebook.**

<p align="center">
<img src="doc/jupyter_notebook_dogs.jpg">
Expand Down Expand Up @@ -366,12 +367,12 @@ If everything has been set up correctly, TensorFlow will initialize the training
</p>

Each step of training reports the loss. It will start high and get lower and lower as training progresses. For my training on the Faster-RCNN-Inception-V2 model, it started at about 3.0 and quickly dropped below 0.8. I recommend allowing your model to train until the loss consistently drops below 0.05, which will take about 40,000 steps, or about 2 hours (depending on how powerful your CPU and GPU are). Note: The loss numbers will be different if a different model is used. MobileNet-SSD starts with a loss of about 20, and should be trained until the loss is consistently under 2.

<!--- I've tried the command below but my port 6006 was closed by default(Windows 10). To make it easier on future learners changed it to 8088, which is open and doesn't need any further actions --->
You can view the progress of the training job by using TensorBoard. To do this, open a new instance of Anaconda Prompt, activate the tensorflow1 virtual environment, change to the C:\tensorflow1\models\research\object_detection directory, and issue the following command:
```
(tensorflow1) C:\tensorflow1\models\research\object_detection>tensorboard --logdir=training
(tensorflow1) C:\tensorflow1\models\research\object_detection>tensorboard --logdir=training/ --host localhost --port 8088
```
This will create a webpage on your local machine at YourPCName:6006, which can be viewed through a web browser. The TensorBoard page provides information and graphs that show how the training is progressing. One important graph is the Loss graph, which shows the overall loss of the classifier over time.
This will create a webpage on your local machine at [localhost:8088](http://localhost:8088/), which can be viewed through a web browser. The TensorBoard page provides information and graphs that show how the training is progressing. One important graph is the Loss graph, which shows the overall loss of the classifier over time.

<p align="center">
<img src="doc/loss_graph.JPG">
Expand Down