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Add bisenetv2. My implementation of BiSeNet

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BiSeNetV1 & BiSeNetV2

My implementation of BiSeNetV1 and BiSeNetV2.

mIOUs and fps on cityscapes val set:

none ss ssc msf mscf fps(fp16/fp32) link
bisenetv1 75.44 76.94 77.45 78.86 68/23 download
bisenetv2 74.95 75.58 76.53 77.08 59/21 download

mIOUs on cocostuff val2017 set:

none ss ssc msf mscf link
bisenetv1 31.49 31.42 32.46 32.55 download
bisenetv2 30.49 30.55 31.81 31.73 download


  1. ss means single scale evaluation, ssc means single scale crop evaluation, msf means multi-scale evaluation with flip augment, and mscf means multi-scale crop evaluation with flip evaluation. The eval scales and crop size of multi-scales evaluation can be found in configs.

  2. The fps is tested in different way from the paper. For more information, please see here.

  3. For cocostuff dataset: The authors of the paper bisenetv2 used the "old split" of 9k train set and 1k val set, while I used the "new split" of 118k train set and 5k val set. Thus the above results on cocostuff does not match the paper. The authors of bisenetv1 did not report their results on cocostuff, so here I simply provide a "make it work" result. Following the tradition of object detection, I used "1x"(90k) and "2x"(180k) schedule to train bisenetv1(1x) and bisenetv2(2x) respectively. Maybe you can have a better result by picking up hyper-parameters more carefully.

  4. The model has a big variance, which means that the results of training for many times would vary within a relatively big margin. For example, if you train bisenetv2 for many times, you will observe that the result of ss evaluation of bisenetv2 varies between 73.1-75.1.

deploy trained models

  1. tensorrt
    You can go to tensorrt for details.

  2. ncnn
    You can go to ncnn for details.


My platform is like this:

  • ubuntu 18.04
  • nvidia Tesla T4 gpu, driver 450.51.05
  • cuda 10.2
  • cudnn 7
  • miniconda python 3.8.8
  • pytorch 1.8.1

get start

With a pretrained weight, you can run inference on an single image like this:

$ python tools/demo.py --config configs/bisenetv2_city.py --weight-path /path/to/your/weights.pth --img-path ./example.png

This would run inference on the image and save the result image to ./res.jpg.

prepare dataset


Register and download the dataset from the official website. Then decompress them into the datasets/cityscapes directory:

$ mv /path/to/leftImg8bit_trainvaltest.zip datasets/cityscapes
$ mv /path/to/gtFine_trainvaltest.zip datasets/cityscapes
$ cd datasets/cityscapes
$ unzip leftImg8bit_trainvaltest.zip
$ unzip gtFine_trainvaltest.zip


Download train2017.zip, val2017.zip and stuffthingmaps_trainval2017.zip split from official website. Then do as following:

$ unzip train2017.zip
$ unzip val2017.zip
$ mv train2017/ /path/to/BiSeNet/datasets/coco/images
$ mv val2017/ /path/to/BiSeNet/datasets/coco/images

$ unzip stuffthingmaps_trainval2017.zip
$ mv train2017/ /path/to/BiSeNet/datasets/coco/labels
$ mv val2017/ /path/to/BiSeNet/datasets/coco/labels

$ cd /path/to/BiSeNet
$ python tools/gen_coco_annos.py

3.custom dataset

If you want to train on your own dataset, you should generate annotation files first with the format like this:


Each line is a pair of training sample and ground truth image path, which are separated by a single comma ,.
Then you need to change the field of im_root and train/val_im_anns in the configuration files. If you found what shows in cityscapes_cv2.py is not clear, you can also see coco.py.


I used the following command to train the models:

# bisenetv1 cityscapes
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file 

# bisenetv2 cityscapes
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file 

# bisenetv1 cocostuff
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file 

# bisenetv2 cocostuff
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file 


  1. though bisenetv2 has fewer flops, it requires much more training iterations. The the training time of bisenetv1 is shorter.
  2. I used overall batch size of 16 to train all models. Since cocostuff has 171 categories, it requires more memory to train models on it. I split the 16 images into more gpus than 2, as I do with cityscapes.

finetune from trained model

You can also load the trained model weights and finetune from it, like this:

$ python -m torch.distributed.launch --nproc_per_node=2 tools/train_amp.py --finetune-from ./res/model_final.pth --config ./configs/bisenetv2_city.py # or bisenetv1

eval pretrained models

You can also evaluate a trained model like this:

$ python tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth

Be aware that this is the refactored version of the original codebase. You can go to the old directory for original implementation if you need, though I believe you will not need it.


Add bisenetv2. My implementation of BiSeNet

License:MIT License


Language:Python 83.3%Language:C++ 9.3%Language:Cuda 6.8%Language:C 0.4%Language:CMake 0.3%