OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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440 lines
15 KiB
440 lines
15 KiB
Collections: |
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- Name: GCNet |
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Metadata: |
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Training Data: COCO |
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Training Techniques: |
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- SGD with Momentum |
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- Weight Decay |
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Training Resources: 8x V100 GPUs |
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Architecture: |
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- Global Context Block |
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- FPN |
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- RPN |
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- ResNet |
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- ResNeXt |
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Paper: |
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URL: https://arxiv.org/abs/1904.11492 |
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Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' |
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README: configs/gcnet/README.md |
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Code: |
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13 |
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Version: v2.0.0 |
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Models: |
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- Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 5.0 |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 39.7 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 35.9 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth |
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- Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 5.1 |
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inference time (ms/im): |
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- value: 66.67 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 39.9 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 36.0 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth |
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- Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 7.6 |
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inference time (ms/im): |
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- value: 87.72 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 41.3 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 37.2 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth |
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- Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 7.8 |
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inference time (ms/im): |
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- value: 86.21 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 42.2 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 37.8 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth |
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- Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py |
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Metadata: |
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Training Memory (GB): 4.4 |
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inference time (ms/im): |
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- value: 60.24 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 38.4 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 34.6 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth |
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- Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 5.0 |
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inference time (ms/im): |
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- value: 64.52 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 40.4 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 36.2 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth |
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- Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 5.1 |
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inference time (ms/im): |
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- value: 66.23 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 40.7 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 36.5 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth |
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- Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py |
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Metadata: |
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Training Memory (GB): 6.4 |
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inference time (ms/im): |
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- value: 75.19 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 40.5 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 36.3 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth |
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- Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 7.6 |
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inference time (ms/im): |
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- value: 83.33 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 42.2 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 37.8 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth |
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- Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 7.8 |
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inference time (ms/im): |
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- value: 84.75 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 42.2 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 37.8 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth |
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- Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py |
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Metadata: |
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Training Memory (GB): 7.6 |
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inference time (ms/im): |
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- value: 88.5 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 42.4 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 37.7 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth |
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- Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 8.8 |
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inference time (ms/im): |
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- value: 102.04 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 43.5 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 38.6 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth |
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- Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 9.0 |
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inference time (ms/im): |
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- value: 103.09 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 43.9 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 39.0 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py |
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Metadata: |
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Training Memory (GB): 9.2 |
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inference time (ms/im): |
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- value: 119.05 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 44.7 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 38.6 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 10.3 |
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inference time (ms/im): |
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- value: 129.87 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 46.2 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 39.7 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Training Memory (GB): 10.6 |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 46.4 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 40.1 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py |
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Metadata: |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 47.5 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 40.9 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 48.0 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 41.3 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth |
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- Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco |
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In Collection: GCNet |
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Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py |
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Metadata: |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 47.9 |
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- Task: Instance Segmentation |
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Dataset: COCO |
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Metrics: |
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mask AP: 41.1 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth
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