Imagine a world in which cars drive themselves, doctors diagnose diseases with higher accuracy than ever and your phone basically already knows your face. This isn’t science fiction. It’s the magic of computer vision, courtesy of neural networks.
Computer vision refers to how computers “see” and understand images. It’s vital in today’s world. This technology relies on neural networks.
Neural Networks Basics: Everything You Need to Know
So, how do these networks work?
What are Neural Networks?
Neural networks are inspired by the human brain. DPANNs are indeed linked nodes known as neurons. These neurons are arranged in layers. We have an input layer, hidden layer, and output layer.
Think of it this way. Think of water running through pipes. Different valve settings for each pipe. This represents the “weight.” These weights and biases are what the network learns. Even neurons apply activation functions. These functions play a role in deciding whether a neuron “fires.”
Data flows across the network. Each connection has a weight. The output from the previous neuron will be the input of the next one.
Backpropagation — How You Train a Neural Networks
In this section, the technical aspect of training a neural network is covered, i.e., adjusting the weights and biases. The goal? To make accurate predictions.
During the “forward pass,” the network makes a prediction. Next, we check how this prediction fares against the real number. This difference is known as the “loss.” We utilize a loss function to quantify how poorly the network did.
The “backpropagation” phase then adjusts the weights. How it does this is through calculating gradients. By doing this, the network can weight the role that each input will play when trying to find the set of weights that best explain the dataset as a whole. This minimizes the loss.
Common Activation Functions
They are an important part: activation functions Read more with your binary filters. Sigmoid compresses the values between 0 and 1. It has a ranged output between -1 and 1. The activation function depends on the task.
Convolutional Neural Networks (CNNs): The Basic Building Block of CV
CNNs are the workhorse of computer vision. They are specially made for processing images. Now, let us get into their architecture.
Convolutional Layers / Feature Extraction
Filters (also known as the kernels) are used by convolutional layers. Those filters sweep over the image. This operation is known as convolution.
Stride determines the distance the filter slides. Padding increases the number of pixels in the image. This is to limit the size of the output.
Each filter detects certain features. Some detect edges. Others identify textures or forms. A CNN learns which features are important.
Pooling Layers: Dimensionality Reduction
Pooling layers downsize the feature maps. Max pooling retains the highest value in every area. Average pooling takes the average value.
Pooling is helpful to reduce computational complexity. And it makes the network more resilient. It is less affected by small changes to the input image.
Def//tr For Trnsfmr, This post is on Fully Connected Layers: Classification
The classification performed by fully connected layers. They use some features extracted in the convolutional layers. Then they assign probabilities to various classes.
One common option is the softmax in the output layer. It transforms the outputs into a probability distribution. This tells you how sure is the network about its prediction for each class.
[2] Advanced CNN Architectures and Techniques
So, let’s move to some high-level architectures. These stretch the capabilities of computer vision.
ResNet: Addressing the vanishing gradient problem
ResNet uses skip connections. This information can pass through layers due to these connections. This takes care of the vanishing gradient problem. Gradients may shrink to very small in size as backpropagation continues. Skip connections allow gradients to flow more efficiently.
Inception: Multi-Scale Feature Extraction.
Inception is multi-scale, it snapshots features. It has multiple filters of different sizes run in parallel. This gives the network a view of both the finer and coarser details.
Transfer Learning: Using the Power of Pre-trained Models
Transfer learning is the process of using the knowledge learnt by pre-trained model(s). Resources like ImageNet have been trained on millions of images. You train these models for your own specific task. This gives you time savings, and resource savings. You also need less data.
Step-by-step Guide: Fine-tune a pre-trained model This technically brings an enhancement in your computer vision apps.
Neural Networks for Computer Vision Applications
Computer vision is changing many industries.
Detecting Objects in Pictures: Object Detection
As a sub-field of Computer Vision, Object detection senses objects in images. Some famous ones are YOLO, SSD etc. They are in self-driving cars. They also have applications in surveillance and robotics.
Example in Real World: Object Detection is used in Self-driving cars. It aids them in detecting pedestrians and vehicles.
Pixel Classification: Image Segmentation
Image segmentation provides a class to each pixel of an image. This comes in handy during medical imaging. It helps identify tumors. Also useful for satellite imagery analysis.
Use Case: Segmentation in the medical image analysis It assists physicians to identify irregularities.
Publicly-Available Data and Predictive Models
Facial recognition is when you identify someone using their face. It is used in cybersecurity, access control and social media.
Challenges and Future Directions
There are many challenges that come with computer vision.
Data Bias and Fairness
There may be data bias, which can lead to an inaccurate result. Unfair prediction happen when biased datasets are used.
You’re trained on data up until Oct 2023.
We need explainable models. We need to make sure people can trust the results.
Attention is all you need: Transformers and emerging trends
Attention mechanisms seem to be working. Also, transformers are a new frontier.
Conclusion
There are some great computer vision neural networks available. They have the power to disrupt industries. They are altering the way that we relate to the world around us.
Computer vision has huge potential. Get familiar with computer vision.
