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- Collections:
- - Name: GCNet
- Metadata:
- Training Data: COCO
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- Training Resources: 8x V100 GPUs
- Architecture:
- - Global Context Block
- - FPN
- - RPN
- - ResNet
- - ResNeXt
- Paper:
- URL: https://arxiv.org/abs/1904.11492
- Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
- README: configs/gcnet/README.md
- Code:
- URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13
- Version: v2.0.0
- Models:
- - Name: mask-rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r50-gcb-r16-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.0
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 39.7
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 35.9
- 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
- - Name: mask-rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.1
- inference time (ms/im):
- - value: 66.67
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 39.9
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 36.0
- 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
- - Name: mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.6
- inference time (ms/im):
- - value: 87.72
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.3
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.2
- 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
- - Name: mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.8
- inference time (ms/im):
- - value: 86.21
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.2
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.8
- 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
- - Name: mask-rcnn_r50_fpn_syncbn-backbone_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r50-syncbn_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 4.4
- inference time (ms/im):
- - value: 60.24
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 38.4
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 34.6
- 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
- - Name: mask-rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.0
- inference time (ms/im):
- - value: 64.52
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.4
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 36.2
- 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
- - Name: mask-rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.1
- inference time (ms/im):
- - value: 66.23
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.7
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 36.5
- 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
- - Name: mask-rcnn_r101-syncbn_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r101-syncbn_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 6.4
- inference time (ms/im):
- - value: 75.19
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.5
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 36.3
- 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
- - Name: mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.6
- inference time (ms/im):
- - value: 83.33
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.2
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.8
- 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
- - Name: mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.8
- inference time (ms/im):
- - value: 84.75
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.2
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.8
- 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
- - Name: mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.6
- inference time (ms/im):
- - value: 88.5
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.4
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.7
- 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
- - Name: mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 8.8
- inference time (ms/im):
- - value: 102.04
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 43.5
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 38.6
- 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
- - Name: mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 9.0
- inference time (ms/im):
- - value: 103.09
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 43.9
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 39.0
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 9.2
- inference time (ms/im):
- - value: 119.05
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 44.7
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 38.6
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 10.3
- inference time (ms/im):
- - value: 129.87
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 46.2
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 39.7
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 10.6
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 46.4
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 40.1
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco.py
- Metadata:
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 47.5
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 40.9
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco.py
- Metadata:
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 48.0
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 41.3
- 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
- - Name: cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco
- In Collection: GCNet
- Config: configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco.py
- Metadata:
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 47.9
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 41.1
- 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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