Image recognition neural network has becomes the mainstream of this digital era. Let’s tackle what is the best one to use.
The Neural Network
So what is Neural Network?
- Firstly, It is a set of algorithms that transform the field of machine learning;
- Also, it is stimulated by biological neural networks;
- The current name is Deep neural network, and it’s established work very well.
- Lastly, neural networks have a general function approximations. And because of that, it applies to many machine learning problems.
Neural Network Architectures
We want to know what is the best image recognition neural network. However, let us all tackle first what are the sample Neural Network Architectures.
Perceptrons
This is the first generation of neural networks. Its computational models are simply fr single neuron only.
Training a Perceptrons.
- It requires back-propagation for network paired data sets. Data sets for input and output.
- The inputs are sent to neurons for processing. Then wait for the output or result.
However, there are errors. These errors if backpropagated with a different variation of input and output.
Convolutional Neural Networks
Convolutional Neural Networks (CNN) develops by Yann LeCun in 1998. The purpose of CNN,
Use for propagation a feedforward net. This feedforward net may have a lot of:
- maps of the replicated unit
- hidden layers
- pooling of outputs
- wide net that copes with characters
Moreover, CNN is different from other networks.
- It is used fundamentally for image processing;
- Also, you can use it for some types of input in audio;
- Moreover, it can be feed with networks images at the same time classifies them; and
- Lastly, It started its journey in scanning.
Short And Long Term Memory
Long and short term memory (LSTM) is developed in 1997 by Hochreiter & Schmidhuer. LSTM networks try to resist the vanishing gradient problem.
They introduce ways and explicitly defines memory cells. These memory cells store the former values.
The LSTM has a two process way.
- Input gate – for adding new stuff
- Output gate – it decides when to pass along the vectors. From the cell -> hidden state.
The Autoencoders
These are designed for unsupervised learning. Such as Unlabeleddata. A decoder can be used for reconstructing the input.
These are just some of the many neural networks.
The Best Image Recognition Neural Network
One of the best neural network for image recognition is Convolutional Neural Network (CNN) as tackle above, it has a lot to offer.
CNN works better in image understanding. Also, the number of parameters is independent of sizes from its origin. And most importantly, you can run a CNN image up to 300×300.
Also, the accuracy level of CNN is up to 95%. Furthermore, it is more accurate than human capabilities.
However, even though it is the best among the rest. CNN has its limitations. Such as:
- It requires high processing power. Therefore, the models are typically trained in high-cost machines. Machines that are specialized in graphical processing units
- The images are failed in rotation and tilted.
