BiSeNet based on pytorch

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Deep LearningBiSeNet
Overview

BiSeNet

BiSeNet based on pytorch 0.4.1 and python 3.6

Dataset

Download CamVid dataset from Google Drive or Baidu Yun(6xw4).

Pretrained model

Download best_dice_loss_miou_0.655.pth in Google Drive or in Baidu Yun(6y3e) and put it in ./checkpoints

Demo

python demo.py

Result

Original GT Predict

Train

python train.py

Use tensorboard to see the real-time loss and accuracy

loss on train

pixel precision on val

miou on val

Test

python test.py

Result

class Bicyclist Building Car Pole Fence Pedestrian Road Sidewalk SignSymbol Sky Tree miou
iou 0.61 0.80 0.86 0.35 0.37 0.59 0.88 0.81 0.28 0.91 0.73 0.655

This time I train the model with dice loss and get better result than cross entropy loss. I did not use lots special training strategy, you can get much better result than this repo if using task-specific strategy.
This repo is mainly for proving the effeciveness of the model.
I also tried some simplified version of bisenet but it seems does not preform very well in CamVid dataset.

Speed

Method 640×320 1280×720 1920×1080
Paper 129.4 47.9 23
This Repo 126.8 53.7 23.6

This shows the speed comparison between paper and my implementation.

  1. The number in first row means input image resolution.
  2. The number in second and third row means FPS.
  3. The result is based on resnet-18.

Future work

  • Finish real-time segmentation with camera or pre-load video

Reference

Owner
GitHub Repository
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