If you’re looking to get started with object detection using TensorFlow, you’ve come to the right place!
TensorFlow Object Detection
The TensorFlow Object Detection API can be used to easily and quickly build an object detection model that runs in a browser, on Android, or on iOS. This article will provide step-by-step instructions on how to build your first TensorFlow object detection mode.
Introduction to object detection
TensorFlow Keras is a powerful tool for creating sophisticated machine learning models. In this tutorial, we'll show you how to use TensorFlow Keras to build an object detection system from scratch. We'll cover everything from installing TensorFlow to training and deploying your model. By the end of this tutorial, you'll be able to build your own state-of-the-art object detection system.
Before we dive into building a model, let's start with a quick introduction to object detection. The idea behind object detection is that you have a camera pointing at an environment and your goal is to identify specific objects in that environment. An example would be identifying different cars on a road. In order to identify cars, you need training data that describes what cars look like from different angles and distances. Then, once you have labeled images of cars, you train a model to take an image from your camera as input and predict if there are any cars in it or not.
Downloading the data set
Before you can start training your own object detection models, you need to have a dataset to train on. TensorFlow doesn't come with any pre-trained models, so you'll need to either create your own or download someone else's. The good news is that there are plenty of free datasets available online. I would recommend the COCO dataset, which can be found here.
-You'll need to download and unzip your dataset into a folder. I created a new folder in my Documents folder called 'tensorflow_models', but you can call it whatever you like. Remember where you save it though, as we'll be using that later. Once your dataset is downloaded, open Terminal (or Command Prompt if you're on Windows). This will open a command line prompt where we can access TensorFlow's installation directory.
Setting up the working directory
The first step is to set up the working directory. I like to keep all my projects in one place, so my directory structure looks like this:
`tensorflow/` - this is where I have my Python installation and TensorFlow source code
`models/` - this is where I'll store the trained models
`data/` - this is where I'll store the training and test data
To get started, create a new directory for your project and navigate into it. Then, create the three subdirectories listed above.
`../tensorflow/` - you should be in your `tensorflow` directory. From here, go into `tensorflow/python` and run `pip install tensorflow==1.3.0rc0`. This will install TensorFlow in your working directory. We will also need to install Python packages for managing images and handling time-series data; for that, use `pip install pillow numpy`. You may have noticed we've been running all our commands so far from inside of our working directory, but what if we wanted to work on code in a different place?
Downloading required libraries
In order to get started with TensorFlow Object Detection, you'll need to first install the required libraries. We'll be using the tensorflow and tensorflow-keras libraries, so go ahead and download them now. If you're not sure how to do this, don't worry! I've included instructions below.
If you're running a Mac, it's as simple as installing Xcode (which you can download here) and then following these instructions. If you're on Linux or Windows, check out these instructions instead. Once you've got everything installed, create a new directory to house your files and navigate into it. Then install TensorFlow's object detection API using pip : pip install tensorflow-object-detection-api . This should place all of the API's Python modules in your site-packages directory within your working directory. Next up, we'll need to update our environment variable so that we can access our python libraries from anywhere on our computer.
Installing prerequisites on Windows 10 using Anaconda
Installing TensorFlow can be a little tricky, especially if you're using Windows. But don't worry, we've got you covered. In this tutorial, we'll show you how to install TensorFlow on Windows 10 using Anaconda.
First, you'll need to install Python. Visit Anaconda's website and download a version for your operating system that matches your computer. Once you've downloaded it, open an Administrator command prompt (go to Start > All Programs > Accessories > Right click on Command Prompt and select Run as administrator). Type C:\Python27\python.exe -m pip install wheel and press Enter. The command will ask if you want to install packages required for compiling additional packages (it may or may not actually compile anything, but just hit y when it prompts you to continue). After everything is done installing, type C:\Python27\python.exe -m pip install --upgrade pip setuptools and press Enter again.
Creating Virtual Environments in Anaconda
Python is a versatile language that can be used for many different things, from web development to data science. TensorFlow is a Python library for machine learning, and Keras is a high-level API for deep learning. In this guide, we'll show you how to set up a Python environment for TensorFlow and Keras on Windows 10 using Anaconda.
You can learn more about using virtual environments in our guide on creating virtual environments in Python. After you've created your environment, you should be able to install TensorFlow and Keras by running pip install tensorflow followed by pip install keras . You can verify that it's installed correctly by running python and typing import tensorflow . If no errors appear, you're ready to move on to creating a neural network!
Downloading model weights with TF Model Optimizer Toolkit
If you're looking to download a pre-trained model with the TensorFlow Model Optimizer Toolkit, there are a few things you'll need to do first. You'll need to have Python installed, as well as the TensorFlow Python package. You can find more information on installing TensorFlow here. Once you have everything installed, you can clone the Model Optimizer repository and install it with the following commands
After installing Model Optimizer, you'll be able to begin downloading pre-trained TensorFlow models with a few simple steps. Downloading these weights allows you to use TensorFlow's high performance code with an already existing model in your projects. For example, you could use the weights from a model trained on ImageNet and use it to train a new image classification network for another project. However, if you want to modify or adapt one of these pre-trained models for your own purposes, as is often recommended for beginners, then using tf.keras may be more appropriate than using Model Optimizer.
Importing TensorFlow r1.2 into Jupyter notebook
Before you can use TensorFlow, you need to install it. The best way to do this is using a package manager like pip. If you're using Anaconda, you can install TensorFlow by running the following command in your terminal:
pip install tensorflow==1.2
Once TensorFlow is installed, you can import it into your Python code. To do this, add the following line to the top of your Python file:
import tensorflow as tf
Then, to run TensorFlow code, you need to initialize it. You can do so by adding a blank line at the end of your Python file, and then running it with python. Then, you can run Tensorflow commands within that environment by prefixing them with tf. So to create a variable called x and assign 5 to it, you'd do something like tf.global_variables_initializer().run() or tf.Variables().update() in Jupyter notebook's cell editor.
Running object detection demo on Windows 10 using Anaconda for Python 3.6
I am going to show you how to run the object detection demo on Windows 10 using Anaconda for Python 3.6. This demo will use the TensorFlow Object Detection API to performobject detection on a given image. The Object Detection API provides pre-trained object detection models that can be used to detect a variety of objects in images.
First, we need to install Anaconda for Python 3.6. You can find the download link here. Once you have downloaded and installed Anaconda, we need to create a new environment for our project.
Open up a Command Prompt and create a new environment called tensorflow_object_detection by typing: conda create -n tensorflow_object_detection python=3.6 anaconda . Next, we need to activate our environment by typing: activate tensorflow_object_detection (Note that when you type these commands in to your Command Prompt, you don't type dollar signs or anything else.)
Also Read:
The Benefits of Deep Learning with Convolutional Neural Networks
Object Detection in Machine Learning: Everything You Need to Know
