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| Collections: | |
| - Name: BiSeNetV2 | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| Paper: | |
| Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic | |
| Segmentation' | |
| URL: https://arxiv.org/abs/2004.02147 | |
| README: configs/bisenetv2/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024 | |
| In Collection: BiSeNetV2 | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.21 | |
| mIoU(ms+flip): 75.74 | |
| Config: configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - BiSeNetV2 | |
| - BiSeNetV2 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 7.64 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json | |
| Paper: | |
| Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic | |
| Segmentation' | |
| URL: https://arxiv.org/abs/2004.02147 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545 | |
| Framework: PyTorch | |
| - Name: bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024 | |
| In Collection: BiSeNetV2 | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.57 | |
| mIoU(ms+flip): 75.8 | |
| Config: configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - BiSeNetV2 | |
| - BiSeNetV2 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 7.64 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json | |
| Paper: | |
| Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic | |
| Segmentation' | |
| URL: https://arxiv.org/abs/2004.02147 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545 | |
| Framework: PyTorch | |
| - Name: bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024 | |
| In Collection: BiSeNetV2 | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.76 | |
| mIoU(ms+flip): 77.79 | |
| Config: configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 32 | |
| Architecture: | |
| - BiSeNetV2 | |
| - BiSeNetV2 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 15.05 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json | |
| Paper: | |
| Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic | |
| Segmentation' | |
| URL: https://arxiv.org/abs/2004.02147 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545 | |
| Framework: PyTorch | |
| - Name: bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024 | |
| In Collection: BiSeNetV2 | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.07 | |
| mIoU(ms+flip): 75.13 | |
| Config: configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - BiSeNetV2 | |
| - BiSeNetV2 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.77 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json | |
| Paper: | |
| Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic | |
| Segmentation' | |
| URL: https://arxiv.org/abs/2004.02147 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545 | |
| Framework: PyTorch | |