# Use pretrained YOLO network for object detection, SJSU data science night (Setup)

This notebook gives step by step instruction to set up the environment to run the codes Use pretrained YOLO network for object detection, SJSU data science night.

# Setup

Please take the following steps in Max OSX (Sorry for Windows users).

## Anaconda 3.7

Please visit Anaconda 3.7 for installation. Note: This will take 2.26GB of space on your computer. The installation took me about 7 MIN.

• Press "Continue" at every step and agree to the terms of the software license agreement.

## Set up virtual environment

A virtual environment is a named, isolated, working copy of Python that that maintains its own files, directories, and paths so that you can work with specific versions of libraries or Python itself without affecting other Python projects.

• ### Let's check if conda is correctly installed. In terminal, type:

conda -V

If conda is installed you should see the conda version. (This step should work if you correctly installed anaconda 3.7).

• ### Make the conda up to date by typing the following command to terminal:

conda update conda

When "Processed ([y]/n)?", answer by hitting y.

• ### Create a virtual environment with name DataScienceNightSJSU

conda create -n DataScienceNightSJSU python=3.6.3 anaconda

• Here, we specify the python version to 3.6.3.
• When "Processed ([y]/n)?", answer by hitting y.

• ### Activate your virtual environment

Activating a conda environment modifies the PATH and shell variables to point to the specific isolated Python set-up you created.

source activate DataScienceNightSJSU

You can also see all available conda environments by typing conda info -e .

#### Refernece:¶

If you want to know the details of each step to create a virtual environment, please visit this website: Create virtual environments for python with conda

# Install three additional python packages: tensorflow, keras, opencv

Anaconda is a convinent package manager that comes with most of the common python packages for do data analysis in python. However, to run my deep learning scripts, you need to separately install three more packages: two deep learning packages (Keras and tensorflow) and a computer vision package, opencv, in your environment.

• Note 1: It is very important to install the correct versions of these modules.
• Note 2: You do NOT need GPU to follow this blog series as we will only use pre-trained network.

To install the three packages in your DataScienceNightSJSU environment, from your terminal run the following codes:

conda install tensorflow==1.9.0
conda install keras==2.1.2
conda install opencv==3.4.2
• When "Processed ([y]/n)?", answer by hitting y.
• Here is how my terminal looks like when I run the above commands in my terminal.

## conda install opencv==3.4.2

To follow the tutorial, you need to download some files that I created.

• ### Step 2: Please download weights_yumi.h5 (194MB) from my Dropbox, and save it in the GitHub repository you just downloaded in Step 1.

• The folder contains the pre-trained YOLO weights named "weights_yumi.h5" (194MB).
• This folder is not part of the GitHub repository because the file size of "weights_yumi.h5" exceeds GitHub's file size limit of 100.00 MB.

• ### Step 3: Open ipython notebook: Open your terminal, (activate the virtual environment that you just created) and then type:

  jupyter notebook

A window will pop up in your browser.

• ### Step 4: Direct to the folder that you just downloaded from GitHub, and open the ipython notebook titled "Step_by_Step_DataScience_Night_Complete.ipynb"

Now you are good to go!