Semantic segmentation in images

In this section, we are going to implement one project on semantic segmentation using a popular network called ENet. 

Efficient Neural Network (ENet) is one of the more popular networks out there due to its ability to perform real-time, pixel-wise semantic segmentation. ENet is up to 18x faster, requires 75x fewer FLOPs, and has 79x fewer parameters than other networks. This means ENet provides better accuracy than the existing models, such as U-Net and SegNet. ENet networks are typically tested on CamVid, CityScapes, and SUN datasets. The model's size is 3.2 MB.

The model we are using has been trained on 20 classes: 

• Road
• Sidewalk
• Building
• Wall
• Fence
• Pole
• TrafficLight
• TrafficSign
• Vegetation
• Terrain
• Sky
• Person
• Rider
• Car
• Truck
• Bus
• Train
• Motorcycle
• Bicycle
• Unlabeled 

We will start with the semantic segmentation project: 

1. First, we will import the necessary packages and libraries, such as numpy, openCV, and...