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Visual Learning - Semantic Segmentation for Self-Driving Cars

Streamlit Application

Image 2

In the code we have implementated two models from scratch

  1. Unet
  2. Deep lab V3
  3. Deep lab V3 - ViT

Results

  1. Unet
  • Train Accuracy: 98.34
  • Train Dice score: 0.83545
  • Test Accuracy 97.24
  • Test Dice score: 0.83259
  1. Deep lab V3 model
  • Train Accuracy: 88.35
  • Train Dice score: 0.79730
  • Test Accuracy: 88.39
  • Test Dice score: 0.79736
  1. Deep lab V3 - ViT model
  • Train Accuracy: 83.97
  • Train Dice score: 0.77760
  • Test Accuracy: 84.07
  • Test Dice score: 0.77762

Test (Left) and Train (right) loss for Deep Lab


Test (Left) and Train (right) loss for Unet
Image 1 Image 2
Test (Left) and Train (right) loss for Deep Lab VIT
Image 2 Image 1

Predictions on Deep Lab

Image 1

Predictions on UNet

Image 2

Predictions on Deep Lab V3 - ViT

Image 2
Image 2