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| Collections: | |
| - Name: ICNet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| README: configs/icnet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 68.14 | |
| mIoU(ms+flip): 70.16 | |
| Config: configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 1.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 71.64 | |
| mIoU(ms+flip): 74.18 | |
| Config: configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 72.51 | |
| mIoU(ms+flip): 74.78 | |
| Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 74.43 | |
| mIoU(ms+flip): 76.72 | |
| Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 68.91 | |
| mIoU(ms+flip): 69.72 | |
| Config: configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 2.53 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.82 | |
| mIoU(ms+flip): 75.67 | |
| Config: configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 74.58 | |
| mIoU(ms+flip): 76.41 | |
| Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.29 | |
| mIoU(ms+flip): 78.09 | |
| Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 70.28 | |
| mIoU(ms+flip): 71.95 | |
| Config: configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 3.08 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.8 | |
| mIoU(ms+flip): 76.1 | |
| Config: configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ICNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.57 | |
| mIoU(ms+flip): 77.86 | |
| Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |
| - Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832 | |
| In Collection: ICNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.15 | |
| mIoU(ms+flip): 77.98 | |
| Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ICNet | |
| - (in1k-pre) | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json | |
| Paper: | |
| Title: ICNet for Real-time Semantic Segmentation on High-resolution Images | |
| URL: https://arxiv.org/abs/1704.08545 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77 | |
| Framework: PyTorch | |