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Semantic-Segmentation

Implemented Semantic Segmentation in PyTorch and Tensorflow.

Dataset

The dataset has been generated as part of the lyft challenge

Sample input and output of the image

__results___7_0

What it does?

The aim of the project is to classify each pixel in the image into one among the classes mentioned in the table below:

Value Tag Description
0 Unlabeled Elements that have not been categorized are considered Unlabeled. This category is meant to be empty or at least contain elements with no collisions.
1 Building Buildings like houses, skyscrapers,... and the elements attached to them.
E.g. air conditioners, scaffolding, awning or ladders and much more.
2 Fence Barriers, railing, or other upright structures. Basically wood or wire assemblies that enclose an area of ground.
3 Other Everything that does not belong to any other category.
4 Pedestrian Humans that walk or ride/drive any kind of vehicle or mobility system.
E.g. bicycles or scooters, skateboards, horses, roller-blades, wheel-chairs, etc.
5 Pole Small mainly vertically oriented pole. If the pole has a horizontal part (often for traffic light poles) this is also considered pole.
E.g. sign pole, traffic light poles.
6 RoadLine The markings on the road.
7 Road Part of ground on which cars usually drive.
E.g. lanes in any directions, and streets.
8 SideWalk Part of ground designated for pedestrians or cyclists. Delimited from the road by some obstacle (such as curbs or poles), not only by markings. This label includes a possibly delimiting curb, traffic islands (the walkable part), and pedestrian zones.
9 Vegetation Trees, hedges, all kinds of vertical vegetation. Ground-level vegetation is considered Terrain.
10 Vehicles Cars, vans, trucks, motorcycles, bikes, buses, trains.
11 Wall Individual standing walls. Not part of a building.
12 TrafficSign Signs installed by the state/city authority, usually for traffic regulation. This category does not include the poles where signs are attached to.
E.g. traffic- signs, parking signs, direction signs...
13 Sky Open sky. Includes clouds and the sun.
14 Ground Any horizontal ground-level structures that does not match any other category. For example areas shared by vehicles and pedestrians, or flat roundabouts delimited from the road by a curb.
15 Bridge Only the structure of the bridge. Fences, people, vehicles, an other elements on top of it are labeled separately.
16 RailTrack All kind of rail tracks that are non-drivable by cars.
E.g. subway and train rail tracks.
17 GuardRail All types of guard rails/crash barriers.
18 TrafficLight Traffic light boxes without their poles.
19 Static Elements in the scene and props that are immovable.
E.g. fire hydrants, fixed benches, fountains, bus stops, etc.
20 Dynamic Elements whose position is susceptible to change over time.
E.g. Movable trash bins, buggies, bags, wheelchairs, animals, etc.
21 Water Horizontal water surfaces.
E.g. Lakes, sea, rivers.
22 Terrain Grass, ground-level vegetation, soil or sand. These areas are not meant to be driven on. This label includes a possibly delimiting curb.

Architecture

This is implemented based on the research paper U-Net: Convolutional Networks

The summary of the architecture as presented in the paper

Capture

Predictions

__results___27_2 __results___27_3

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This repository contains the implementation of U-nets from scratch in PyTorch and TensorFlow

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