For the past decades, Machine Learning researchers have led many different studies not only meant to make our lives easier but also to improve the productivity and efficiency of certain fields of the economy. Artificial Intelligence and Object Detection are particularly interesting for them. Thanks to their dedicated work, many businesses and activities have been able to introduce AI in their internal processes. Health professionals use it to detect tumors or abnormalities during medical exams involving the recording of images (such as X-rays or ultrasound scans). Airport Security agents use it to detect any suspicious behavior from a passenger or potentially unattended luggage. Self-driving cars are even using it to detect the presence of obstacles like bicycles, other cars, or even pedestrians.
Image Recognition, a.k.a Object Detection, has changed our lives and the way we work. But before getting to the part when an Image Recognition app is released, it needs to go through a whole and complex process. One of the steps is absolutely crucial: training the object detection model and validating the results. This article will stress the importance of training such a tool before using it. It will also review the three steps which need to be followed to get the best results possible, and it will dive into the details of the model training techniques that are used for Object Detection.
Why is it important to train your Image Recognition application?
Nowadays Computer Vision and Artificial Intelligence have become very important industries. It is known to use very efficient tools and to be able to give an answer to a lot of different issues. Image Recognition is beginning to have a key position in today’s society. Many companies’ CEOs truly believe it represents the future of their activities, and have already started applying it to their system.
Image Recognition (or Object Detection) mainly relies on the way human beings interact with their environment. This specific task uses different techniques to copy the way the human visual cortex works. These various methods take an image or a set of many images input into a neural network. They then output zones usually delimited by rectangles with labels that respectively define the location and the category of the objects in the image.
Training the model is crucial to allow it to perform correctly. Artificial Intelligence and Machine Learning consider that when you install and set up a new application, it needs to be trained on a certain number of labeled samples to learn the capability to identify the objects from the image. Since it relies on the imitation of the human brain, it is important to make sure it will show the same (or better) results than a person would do. Object Detection is a process that requires the same training as someone who would learn something new. The more you practice activity, the better you become. It works exactly the same with computer devices and programs.
It is only when the trained model complies with various rules, that the data scientist or the project manager will validate the process and say it is ready to run on its own.
Now let’s go over the three necessary steps to train Image Recognition.
Three steps to follow to train Image Recognition thoroughly
You have decided to introduce Image Recognition into the system of your company. If you go through a Supervised approach, which is recommended to obtain accurate results. , a training phase is necessary. It will allow you to analyze the results and make sure they correspond to the output you were looking for.
Step 1: Preparation of the training dataset
Training your object detection model from scratch requires a consequent image database. Many free datasets are available for download on the Keras platform. After this, you will probably have to go through data augmentation in order to avoid overfitting objects during the training phase. Data augmentation consists in enlarging the image library, by creating new references. Changing the orientation of the pictures, changing their colors to greyscale, or even blurring them. All these options create new data and allow the system to analyze the images more easily.
Once you have entered your data, a specific format will have to be used. Formatting images is essential for your machine learning program because it needs to understand all of them. If the quality or dimensions of the pictures vary too much, it will be quite challenging and time-consuming for the system to process everything.
When the formatting is done, you will need to tell your model what classes of objects you want it to detect and classify. The minimum number of images necessary for an effective training phase is 200. When installing Kili, you will be able to annotate the images from an image dataset and create the various categories you will need.
After the classes are saved and the images annotated, you will have to clearly identify the location of the objects in the images. Using bounding boxes is then necessary. You will just have to draw rectangles around the objects you need to identify and select the matching classes.
Step 2: Preparation and understanding of how Convolutional Neural Network models work
Image Recognition applications usually work with Convolutional Neural Network models. This is what you will have to use when training your app.
As we know, Machines don’t see an image as a whole, it will analyze the data which come out of it: the pixels. Neural Networks, which imitate the actions of human neurons, are acting as feature extractors. They will extract features directly from the pictures, and introduce them to the system for analysis. When the data and images are correctly annotated, it helps the model to pick out interesting features in order to give a correct classification. This is the role of Convolutional Neural Networks or CNNs.
Before installing a CNN algorithm, you should get some more details about the complex architecture of this particular model, and the way it works.
CNNs' architecture is composed of various layers which are meant to lead different actions. The model will first take all the pixels of the picture and apply a first filter or layer called a convolutional layer. When taking all the pixels, the layer will extract some of the features from them. This will create a feature map, enabling the first step to object detection and recognition. Many more Convolutional layers can be applied depending on the number of features you want the model to examine (the shapes, the colors, the textures which are seen in the picture, etc).
When all the data has been analyzed and gathered in a feature map, an activation layer is applied. This one is meant to simplify the results, allowing the algorithm to process them more rapidly.
To make the method even more efficient, pooling layers are applied during the process. These are meant to gather and compress the data from the images and to clean them before using other layers. These are very important as they avoid overfitting, which can prevent the model from recognizing two elements that could be overlapping in the picture (for example a girl carrying a bag and standing in front of a car). Pooling layers are a great way to increase the accuracy of a CNN model.
Lastly, flattening and fully connected layers are applied to the images, in order to combine all the input features and results. This step is essential for Image Recognition.
These Convolutional Neural Networks are being trained for a reason. After the training of all these layers on the training data, and if the results are satisfying, the Image Recognition application can be launched. But there is one element you should consider: the longer you train your model, the better the performance and accuracy of your app will be.
Step 3: Evaluation and validation of the training results of your system
Before using your Image Recognition model for good, going through an evaluation and validation process is extremely important. It will allow you to make sure your solution matches a required level of performance for the system it is integrated into.
Your model has been trained but now you want to evaluate the results of this training phase. A different dataset has to be used, and evaluating the trained model will tell you whether the training phase has been successful or not. This new dataset is unknown to your algorithm and is called the validation dataset
Use the results from the analysis of this new set of images and pictures with the one from the training phase to compare their accuracy and performance when identifying and classifying the images.
If you notice a difference between the various outputs, you might want to check your algorithm again and proceed with a new training phase. But this time, maybe you should modify some of the parameters you have applied in the first session of training. Maybe the problem relies on the format of pictures which is not the same for every image. Or it can be the result of a lack of variation in the pictures. In this case, you should try making data augmentation in order to propose a larger database. It could even be a problem regarding the labeling of your classes, which might not be clear enough for example.
Once the new training phase brings you satisfaction, you should always go through a very last testing phase. For this one, you are going to confront your algorithm with the third set of data: the test set. One might think that this final test is not so important but you may have over-optimized the parameters on the validation dataset. You absolutely need to check if those modifications have been successful or not. Thus, a very last test with unknown sets of pictures is necessary. It is also the occasion to validate both the accuracy of the program and its rapidness in processing the images.
This article gives you a lot of details about the way training Image Recognition works. We encourage you to follow the whole tutorial published on the website https://www.tensorflow.org/ to get more information regarding the various lines of code to use for models with Python setups.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
Why is Image Recognition so interesting for people?
For years now, Artificial Intelligence has proven to be quite effective. It can truly face issues and solve them the way a human being would. Image Recognition is indeed one of the major topics covered by this field of Computer Science. It allows us to extract as much information as we want from a picture and has the ability to be applied to multiple areas of businesses.
A tool that can be applied to many different fields of activity
According to market research-led and published on the website https://www.marketsandmarkets.com/, the Image Recognition market has increased at a rate of 19.5%, in only five years.
Thanks to the rise of smartphones, together with social media, images have taken the lead in terms of digital content. It is now so important that an extremely important part of Artificial Intelligence is based on analyzing pictures. Nowadays, it is applied to various activities and for different purposes.
Self-driving cars are a true revolution. It seems to be quite futuristic for a lot of people: watching cars able to drive passengers without seeing them even touching the steering wheel or the pedals. With the help of cameras all around the device, radars, and sensors, the car is able to determine which are the elements present in its surrounding area and make predictions regarding their trajectory or actions. The neural networks within the program analyze the pixel patterns from the images of cameras and can tell whether the object on the right-hand side is a bicycle or not and if it is coming towards the car or going away from it. They also detect and identify traffic signs and signals, trees, pathways, or even pedestrians.
Home Security has become a huge preoccupation for people as well as Insurance Companies. Robberies happen every day to many different people. Many individuals have decided to tackle this problem. They started to install cameras and security alarms all over their homes and surrounding areas. This has proven to be very efficient to a lot of people. Most of the time, it is used to show the Police or the Insurance Company that a thief indeed broke into the house and robbed something. But this solution is also used to detect a lot of fraud. On another note, CCTV cameras are more and more installed in big cities to spot incivilities and vandalism for instance. CCTV camera devices are also used by stores to highlight shoplifters in actions and provide the Police authorities with proof of the felony. Lastly, Airport Security agents are using this kind of camera as well so as to detect suspicious behavior of individuals, to practice facial recognition, and to identify potential threats such as the presence of unattended bags.
Medical staff members seem to be appreciating more and more the application of AI in their field. Through X-rays for instance, Image annotations can detect and put bounding boxes around fractures, abnormalities, or even tumors. Thanks to Object Detection, doctors are able to give their patients their diagnostics more rapidly and more accurately. They can check if their treatment is functioning properly or not, and they can even recognize the age of certain bones.
Retail, e-commerce, and Marketing
Since the beginning of the COVID-19 pandemic and the lockdown it has implied, people have started to place orders on the Internet for all kinds of items (clothes, glasses, food, etc.). Some companies have developed their own AI algorithm for their specific activities. Online shoppers now have the possibility to try clothes or glasses online. They just have to take a video or a picture of their face or body to get try items they choose online directly through their smartphones. This way, the customer can visualize how the items look on him or her. The person just has to place the order on the items he or she is interested in. Online shoppers also receive suggestions of pieces of clothing they might enjoy, based on what they have searched for, purchased, or shown interest in.
Farmers are always looking for new ways to improve their working conditions. Taking care of both their cattle and their plantation can be time-consuming and not so easy to do. Today more and more of them use AI and Image Recognition to improve the way they work. Cameras inside the buildings allow them to monitor the animals, make sure everything is fine. When animals give birth to their babies, farmers can easily identify if it is having difficulties delivering and can quickly react and come to help the animal. These professionals also have to deal with the health of their plantations. Object Detection helps them to analyze the condition of the plant and gives them indications to improve or save the crops, as they will need it to feed their cattle.
Some elements to keep in mind when choosing an Image Recognition app
Your company is currently thinking about using Object Detection for your business? Now you know how to deal with it, more specifically with its training phase.
Many activities can adapt these Image Processing tools to make their businesses more effectively. Choosing a solution has to be thought through. Here are some tips for you to consider when you want to get your own application.
The accuracy of the algorithm is what comes first. Prepare all your labels and test your data with different models and solutions. Comparing several solutions will allow you to see if the output is accurate enough for the use you want to make with it. Making several comparisons are a good way to identify your perfect solution.
Object Detection is based on Machine Learning programs, so the goal of such an application is to be able to predict and learn by itself. Be sure to pick a solution that guarantees a certain ability to adapt and learn.
An effective Object Detection app should be fast enough, so the chosen model should be as well.
One should also check that the model can adapt to future needs. Today’s conditions for the model to function properly might not be the same in 2 or 3 years. And your business might also need to apply more functions to it in a few years.
Simplicity is the key. Artificial Intelligence and Computer Vision might not be easy to understand for users who have never got into details of these fields. This is why choosing an easy-to-understand and set-up method should be a strong criterion to consider. If you don’t have internal qualified staff to be in charge of your AI application, you might have to dive into it to find some information. So choosing a solution easy to set up could be of great help for its users.
Image Recognition algorithms and applications are becoming prominent topics for many organizations. They are now able to improve their productivity and make giant steps in their own fields. Training your program reveals to be absolutely essential in order to have the best results possible.