My implementation of BiSeNetV1 and BiSeNetV2.

mIOUs and fps on cityscapes val set:

mIOUs on cocostuff val2017 set:

Tips:

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. The authors of bisenetv2 used cocostuff-10k, while I used cocostuff-123k(do not know how to say, just same 118k train and 5k val images as object detection). Thus the results maybe different from paper.

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.

1. tensorrt
   You can go to tensorrt for details.

2. ncnn
   You can go to ncnn for details.

3. openvino
   You can go to openvino for details.

4. tis
   Triton Inference Server(TIS) provides a service solution of deployment. You can go to tis for details.

My platform is like this:

• ubuntu 18.04
• nvidia Tesla T4 gpu, driver 450.80.02
• cuda 10.2/11.3
• cudnn 8
• miniconda python 3.8.8
• pytorch 1.11.0

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.

Or you can run inference on a video like this:

$ python tools/demo_video.py --config configs/bisenetv2_coco.py --weight-path res/model_final.pth --input ./video.mp4 --output res.mp4

This would generate segmentation file as res.mp4. If you want to read from camera, you can set --input camera_id rather than input ./video.mp4.

1.cityscapes

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

2.cocostuff

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:

munster_000002_000019_leftImg8bit.png,munster_000002_000019_gtFine_labelIds.png
frankfurt_000001_079206_leftImg8bit.png,frankfurt_000001_079206_gtFine_labelIds.png
...

Each line is a pair of training sample and ground truth image path, which are separated by a single comma ,.

I recommand you to check the information of your dataset with the script:

$ python tools/check_dataset_info.py --im_root /path/to/your/data_root --im_anns /path/to/your/anno_file

This will print some of the information of your dataset.

Then you need to change the field of im_root and train/val_im_anns in the config file. I prepared a demo config file for you named bisenet_customer.py. You can start from this conig file.

Training commands I used to train the models can be found in here.

Note:

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.

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

$ export CUDA_VISIBLE_DEVICES=0,1
$ torchrun --nproc_per_node=2 tools/train_amp.py --finetune-from ./res/model_final.pth --config ./configs/bisenetv2_city.py # or bisenetv1

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

or you can use multi gpus:

$ torchrun --nproc_per_node=2 tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth