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/configs/dnlnet/dnlnet.yml
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| 1 | Collections: |
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| 2 | - Name: dnlnet |
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| 3 | Metadata: |
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| 4 | Training Data: |
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| 5 | - Cityscapes |
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| 6 | - ADE20K |
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| 7 | Paper: |
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| 8 | URL: https://arxiv.org/abs/2006.06668 |
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| 9 | Title: Disentangled Non-Local Neural Networks |
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| 10 | README: configs/dnlnet/README.md |
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| 11 | Code: |
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| 12 | URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88 |
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| 13 | Version: v0.17.0 |
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| 14 | Converted From: |
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| 15 | Code: https://github.com/yinmh17/DNL-Semantic-Segmentation |
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| 16 | Models: |
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| 17 | - Name: dnl_r50-d8_512x1024_40k_cityscapes |
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| 18 | In Collection: dnlnet |
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| 19 | Metadata: |
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| 20 | backbone: R-50-D8 |
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| 21 | crop size: (512,1024) |
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| 22 | lr schd: 40000 |
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| 23 | inference time (ms/im): |
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| 24 | - value: 390.62 |
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| 25 | hardware: V100 |
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| 26 | backend: PyTorch |
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| 27 | batch size: 1 |
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| 28 | mode: FP32 |
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| 29 | resolution: (512,1024) |
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| 30 | Training Memory (GB): 7.3 |
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| 31 | Results: |
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| 32 | - Task: Semantic Segmentation |
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| 33 | Dataset: Cityscapes |
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| 34 | Metrics: |
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| 35 | mIoU: 78.61 |
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| 36 | Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py |
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| 37 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth |
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| 38 | - Name: dnl_r101-d8_512x1024_40k_cityscapes |
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| 39 | In Collection: dnlnet |
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| 40 | Metadata: |
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| 41 | backbone: R-101-D8 |
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| 42 | crop size: (512,1024) |
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| 43 | lr schd: 40000 |
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| 44 | inference time (ms/im): |
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| 45 | - value: 510.2 |
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| 46 | hardware: V100 |
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| 47 | backend: PyTorch |
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| 48 | batch size: 1 |
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| 49 | mode: FP32 |
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| 50 | resolution: (512,1024) |
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| 51 | Training Memory (GB): 10.9 |
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| 52 | Results: |
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| 53 | - Task: Semantic Segmentation |
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| 54 | Dataset: Cityscapes |
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| 55 | Metrics: |
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| 56 | mIoU: 78.31 |
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| 57 | Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py |
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| 58 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth |
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| 59 | - Name: dnl_r50-d8_769x769_40k_cityscapes |
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| 60 | In Collection: dnlnet |
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| 61 | Metadata: |
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| 62 | backbone: R-50-D8 |
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| 63 | crop size: (769,769) |
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| 64 | lr schd: 40000 |
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| 65 | inference time (ms/im): |
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| 66 | - value: 666.67 |
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| 67 | hardware: V100 |
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| 68 | backend: PyTorch |
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| 69 | batch size: 1 |
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| 70 | mode: FP32 |
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| 71 | resolution: (769,769) |
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| 72 | Training Memory (GB): 9.2 |
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| 73 | Results: |
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| 74 | - Task: Semantic Segmentation |
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| 75 | Dataset: Cityscapes |
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| 76 | Metrics: |
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| 77 | mIoU: 78.44 |
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| 78 | mIoU(ms+flip): 80.27 |
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| 79 | Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py |
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| 80 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth |
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| 81 | - Name: dnl_r101-d8_769x769_40k_cityscapes |
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| 82 | In Collection: dnlnet |
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| 83 | Metadata: |
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| 84 | backbone: R-101-D8 |
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| 85 | crop size: (769,769) |
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| 86 | lr schd: 40000 |
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| 87 | inference time (ms/im): |
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| 88 | - value: 980.39 |
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| 89 | hardware: V100 |
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| 90 | backend: PyTorch |
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| 91 | batch size: 1 |
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| 92 | mode: FP32 |
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| 93 | resolution: (769,769) |
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| 94 | Training Memory (GB): 12.6 |
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| 95 | Results: |
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| 96 | - Task: Semantic Segmentation |
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| 97 | Dataset: Cityscapes |
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| 98 | Metrics: |
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| 99 | mIoU: 76.39 |
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| 100 | mIoU(ms+flip): 77.77 |
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| 101 | Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py |
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| 102 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth |
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| 103 | - Name: dnl_r50-d8_512x1024_80k_cityscapes |
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| 104 | In Collection: dnlnet |
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| 105 | Metadata: |
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| 106 | backbone: R-50-D8 |
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| 107 | crop size: (512,1024) |
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| 108 | lr schd: 80000 |
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| 109 | Results: |
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| 110 | - Task: Semantic Segmentation |
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| 111 | Dataset: Cityscapes |
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| 112 | Metrics: |
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| 113 | mIoU: 79.33 |
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| 114 | Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py |
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| 115 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth |
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| 116 | - Name: dnl_r101-d8_512x1024_80k_cityscapes |
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| 117 | In Collection: dnlnet |
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| 118 | Metadata: |
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| 119 | backbone: R-101-D8 |
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| 120 | crop size: (512,1024) |
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| 121 | lr schd: 80000 |
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| 122 | Results: |
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| 123 | - Task: Semantic Segmentation |
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| 124 | Dataset: Cityscapes |
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| 125 | Metrics: |
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| 126 | mIoU: 80.41 |
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| 127 | Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py |
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| 128 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth |
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| 129 | - Name: dnl_r50-d8_769x769_80k_cityscapes |
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| 130 | In Collection: dnlnet |
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| 131 | Metadata: |
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| 132 | backbone: R-50-D8 |
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| 133 | crop size: (769,769) |
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| 134 | lr schd: 80000 |
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| 135 | Results: |
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| 136 | - Task: Semantic Segmentation |
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| 137 | Dataset: Cityscapes |
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| 138 | Metrics: |
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| 139 | mIoU: 79.36 |
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| 140 | mIoU(ms+flip): 80.7 |
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| 141 | Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py |
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| 142 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth |
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| 143 | - Name: dnl_r101-d8_769x769_80k_cityscapes |
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| 144 | In Collection: dnlnet |
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| 145 | Metadata: |
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| 146 | backbone: R-101-D8 |
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| 147 | crop size: (769,769) |
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| 148 | lr schd: 80000 |
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| 149 | Results: |
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| 150 | - Task: Semantic Segmentation |
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| 151 | Dataset: Cityscapes |
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| 152 | Metrics: |
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| 153 | mIoU: 79.41 |
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| 154 | mIoU(ms+flip): 80.68 |
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| 155 | Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py |
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| 156 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth |
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| 157 | - Name: dnl_r50-d8_512x512_80k_ade20k |
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| 158 | In Collection: dnlnet |
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| 159 | Metadata: |
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| 160 | backbone: R-50-D8 |
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| 161 | crop size: (512,512) |
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| 162 | lr schd: 80000 |
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| 163 | inference time (ms/im): |
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| 164 | - value: 48.4 |
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| 165 | hardware: V100 |
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| 166 | backend: PyTorch |
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| 167 | batch size: 1 |
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| 168 | mode: FP32 |
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| 169 | resolution: (512,512) |
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| 170 | Training Memory (GB): 8.8 |
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| 171 | Results: |
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| 172 | - Task: Semantic Segmentation |
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| 173 | Dataset: ADE20K |
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| 174 | Metrics: |
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| 175 | mIoU: 41.76 |
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| 176 | mIoU(ms+flip): 42.99 |
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| 177 | Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py |
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| 178 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth |
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| 179 | - Name: dnl_r101-d8_512x512_80k_ade20k |
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| 180 | In Collection: dnlnet |
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| 181 | Metadata: |
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| 182 | backbone: R-101-D8 |
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| 183 | crop size: (512,512) |
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| 184 | lr schd: 80000 |
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| 185 | inference time (ms/im): |
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| 186 | - value: 79.74 |
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| 187 | hardware: V100 |
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| 188 | backend: PyTorch |
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| 189 | batch size: 1 |
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| 190 | mode: FP32 |
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| 191 | resolution: (512,512) |
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| 192 | Training Memory (GB): 12.8 |
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| 193 | Results: |
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| 194 | - Task: Semantic Segmentation |
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| 195 | Dataset: ADE20K |
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| 196 | Metrics: |
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| 197 | mIoU: 43.76 |
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| 198 | mIoU(ms+flip): 44.91 |
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| 199 | Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py |
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| 200 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth |
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| 201 | - Name: dnl_r50-d8_512x512_160k_ade20k |
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| 202 | In Collection: dnlnet |
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| 203 | Metadata: |
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| 204 | backbone: R-50-D8 |
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| 205 | crop size: (512,512) |
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| 206 | lr schd: 160000 |
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| 207 | Results: |
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| 208 | - Task: Semantic Segmentation |
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| 209 | Dataset: ADE20K |
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| 210 | Metrics: |
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| 211 | mIoU: 41.87 |
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| 212 | mIoU(ms+flip): 43.01 |
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| 213 | Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py |
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| 214 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth |
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| 215 | - Name: dnl_r101-d8_512x512_160k_ade20k |
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| 216 | In Collection: dnlnet |
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| 217 | Metadata: |
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| 218 | backbone: R-101-D8 |
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| 219 | crop size: (512,512) |
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| 220 | lr schd: 160000 |
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| 221 | Results: |
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| 222 | - Task: Semantic Segmentation |
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| 223 | Dataset: ADE20K |
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| 224 | Metrics: |
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| 225 | mIoU: 44.25 |
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| 226 | mIoU(ms+flip): 45.78 |
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| 227 | Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py |
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| 228 | Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth |