In this article, we will tackle how to choose the best image recognition architecture. Also, let us know the information about image recognition.
Best Image Recognition Architecture
What is image recognition? Image recognition is a computer that can recognize the objects and what is in the image.
So the images are used to recognize the objects. Here, we will provide you with the details related to how to choose the best image recognition architecture.
How To Choose The Best Image Recognition Architecture?
There are many types of architecture for recognizing images. Here are the types of architecture from the following:
Deep Convolutional Neural Network (CNN)
Object Detection Convolutional Neural Network (CNN)
Bi-directional R-CNN
Faster R-CNN
MobileNets
YOLO
YOLOv2
SSD
ResNet
VGG16
VGG19
ResNet50
ResNet101
DenseNet
DenseNet161
DenseNet201
DenseNet121
DenseNET201
SqueezeDet
MobileNet
Large Scale Visual Recognition Challenge (ILSVRC)
Now, let us find out what you need to look at. While choosing the best image recognition architecture for your project.
Here are the things you need to look at the following:
1) Accuracy
Accuracy is one of the most important elements of choosing an image recognition architecture. Many factors affect accuracy.
Like training data sets and model complexity, etc. You should always choose a model that has high accuracy.
So you should not compromise on accuracy while designing your project. Also, you should always check whether your model is performing well or not.
So by using metrics like precision and recall.
2) Model Complexity
The model complexity is another important element. When choosing an image recognition architecture for your project.
It depends on various factors like the type of project, accuracy factor. Also, training data set size, network topology, etc.
Most neural network topologies are used for image recognition architectures. Models with low complexity are easy to understand & implement.
But they have lower accuracy compared to models with higher complexity. It is because more parameters are used in higher complexity models.
Which helps them in increasing their performance factor. So, you should choose a model according to your project requirement & goals.
While using deep learning models in your projects. So you should always try to use simpler models with high performance.
So that it will help you in getting better results when comparing. With complex models having low performance which will help you in getting bad results.
3) Training Data Set
Training data sets is another important factor for image recognition architecture. You should always use images of high resolution for image recognition architectures.
It is to get accurate results. Also, the number of images used for training data sets is a very important factor.
Because the accuracy level of the model depends upon the number of images. That is used for training the data set.
Also, various factors affect the accuracy level like the following:
- Image Size,
- Image Color,
- Image Resolution,
- Number of Images,
- Number of Classes etc.
So you should always try to use more images for training data sets to get accurate results. Also, you should analyze the images used for training data sets.
Whether they are genuine or not. So it will help you in getting accurate results when comparing with other models.
Which uses only a few images for training data sets. Also, you should always try to generate your images using your project requirements like objects, faces, etc.
So that it will help you in getting better results when comparing. With other models which use only a few images for training data sets.
