configs/backup/gcnet/metafile.yml

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