4003b8f421
* BiSeNetV2 first commit * BiSeNetV2 unittest * remove pytest * add pytest module * fix ConvModule input name * fix pytest error * fix unittest * refactor * BiSeNetV2 Refactory * fix docstrings and add some small changes * use_sigmoid=False * fix potential bugs about upsampling * Use ConvModule instead * Use ConvModule instead * fix typos * fix typos * fix typos * discard nn.conv2d * discard nn.conv2d * discard nn.conv2d * delete **kwargs * uploading markdown and model * final commit * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * Fix README conflict * Fix unittest problem * Fix unittest problem * BiSeNetV2 * Fixing fps * Fixing typpos * bisenetv2
4.3 KiB
4.3 KiB
Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
Introduction
@article{yu2021bisenet,
title={Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation},
author={Yu, Changqian and Gao, Changxin and Wang, Jingbo and Yu, Gang and Shen, Chunhua and Sang, Nong},
journal={International Journal of Computer Vision},
pages={1--18},
year={2021},
publisher={Springer}
}
Results and models
Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|---|---|---|---|---|---|---|---|---|---|
| BiSeNetV2 | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | 31.77 | 73.21 | 75.74 | config | model | log |
| BiSeNetV2 (OHEM) | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | - | 73.57 | 75.80 | config | model | log |
| BiSeNetV2 (4x8) | BiSeNetV2 | 1024x1024 | 160000 | 15.05 | - | 75.76 | 77.79 | config | model | log |
| BiSeNetV2 (FP16) | BiSeNetV2 | 1024x1024 | 160000 | 5.77 | 36.65 | 73.07 | 75.13 | config | model | log |
Note:
OHEMmeans Online Hard Example Mining (OHEM) is adopted in training.FP16means Mixed Precision (FP16) is adopted in training.4x8means 4 GPUs with 8 samples per GPU in training.