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