Collections: - Name: BiSeNetV1 License: Apache License 2.0 Metadata: Training Data: - Cityscapes - COCO-Stuff 164k Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 README: configs/bisenetv1/README.md Frameworks: - PyTorch Models: - Name: bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 74.44 mIoU(ms+flip): 77.05 Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py Metadata: Training Data: Cityscapes Batch Size: 16 Architecture: - R-18-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 5.69 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 74.37 mIoU(ms+flip): 76.91 Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py Metadata: Training Data: Cityscapes Batch Size: 16 Architecture: - R-18-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 5.69 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.16 mIoU(ms+flip): 77.24 Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py Metadata: Training Data: Cityscapes Batch Size: 32 Architecture: - R-18-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 11.17 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.92 mIoU(ms+flip): 78.87 Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py Metadata: Training Data: Cityscapes Batch Size: 16 Architecture: - R-50-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 15.39 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.68 mIoU(ms+flip): 79.57 Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py Metadata: Training Data: Cityscapes Batch Size: 16 Architecture: - R-50-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 15.39 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 25.45 mIoU(ms+flip): 26.15 Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-18-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 28.55 mIoU(ms+flip): 29.26 Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-18-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 6.33 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 29.82 mIoU(ms+flip): 30.33 Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-50-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 34.88 mIoU(ms+flip): 35.37 Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-50-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 9.28 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 31.14 mIoU(ms+flip): 31.76 Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-101-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch - Name: bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Results: Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 37.38 mIoU(ms+flip): 37.99 Config: configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py Metadata: Training Data: COCO-Stuff 164k Batch Size: 16 Architecture: - R-101-D32 - BiSeNetV1 Training Resources: 4x V100 GPUS Memory (GB): 10.36 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json Paper: Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' URL: https://arxiv.org/abs/1808.00897 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 Framework: PyTorch