BiSeNet based on pytorch 0.4.1 and python 3.6
Download CamVid dataset from Google Drive or Baidu Yun(6xw4).
Download best_dice_loss_miou_0.655.pth in Google Drive or in Baidu Yun(6y3e) and put it in ./checkpoints
python demo.py | Original | GT | Predict |
|---|---|---|
 |  |  |
python train.py Use tensorboard to see the real-time loss and accuracy
python test.py | 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.
- Finish real-time segmentation with camera or pre-load video