There’s always room for improvement to what others favorite motto. So in this article let’s tackle how can we improve our image recognition.
Image Recognition Works As
Image recognition offers a lot of technological advantages. Also, its applications are already amazingly huge. From individuals up to companies.
Many are using image recognition apps as one of their security systems. So, as one of the companies that depend on image recognition as a security system, we always want the best.
We always wanted the most improved and updated system to protect what we need to protect.
For example in surveillance security. We don’t want that hackers can easily infiltrate the system then mess us.
However, many hackers can easily push their way.
Improve Your Image Recognition
Building a convolutional neural network or Cnn and training a model is perform by metrics. So one of the keys to improving your image recognition is to improve your metrics.
These metrics will boost the representation of your models. Let us split it into two categories, the easiest way, and the difficult way.
The Easiest Way
- Adding more data – adding data to your model is one of the easiest ways. Also, it is very helpful in creating a deep network.
- Adding more layers – having a complex dataset it comes with utilizing. Using the power of deep networks you can add some more layers to your architecture.
Moreover, these added layers will let you network learn complex classification function. Furthermore, it will improve the classification display.
- Increasing and Decreasing the image size – in processing the image training evaluation it needs experimentation.
- Allot more time in training – Take it easy. Take your time. One way to improve your image recognition performance is taking time to analyze the metrics.
The Difficult Way
- Transferring learning – Minimize the learning about edges and lines. Also, use the Residual Network or ResNet. In your workflow download and load it. Also, you can connect the layer. It claims to have a 71% up to 95% accuracy level.
- Data Augmentation – Adding synthetic data to the dataset. Such as flipping images, adding noise, or any lies that could ruin the data.
- Changing Kernel sizes and activation functions – In this process there are a lot of mathematical involved. Therefore, make sure the way you transform the data properly.
But for those machine learning researchers, it kind of a piece of cake. Moreover, it could be a solution for them to improve their image recognition.
Why We Need To Improve Image Recognition
Fact is Adversarial is one of the processes that hackers than to infiltrate the image recognition system.
So, what are the adversarial attacks? It is the instances where the small or intentional feature distress. By these, the machine learning model is making a false prediction.
By these, the machine learning algorithms can be fooled and take advantage of hackers. This is just one of the reasons why we need to improve our image recognition.
