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- Collections:
- - Name: Deformable Convolutional Networks
- Metadata:
- Training Data: COCO
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- Training Resources: 8x V100 GPUs
- Architecture:
- - Deformable Convolution
- Paper:
- URL: https://arxiv.org/abs/1811.11168
- Title: 'Deformable ConvNets v2: More Deformable, Better Results'
- README: configs/dcn/README.md
- Code:
- URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15
- Version: v2.0.0
-
- Models:
- - Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 4.0
- inference time (ms/im):
- - value: 56.18
- 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
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth
-
- - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 4.1
- inference time (ms/im):
- - value: 56.82
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.4
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth
-
- - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
- Metadata:
- Training Memory (GB): 4.2
- inference time (ms/im):
- - value: 57.47
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth
-
- - Name: faster_rcnn_r50_fpn_dpool_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
- Metadata:
- Training Memory (GB): 5.0
- inference time (ms/im):
- - value: 58.14
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 38.9
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth
-
- - Name: faster_rcnn_r50_fpn_mdpool_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
- Metadata:
- Training Memory (GB): 5.8
- 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: 38.7
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth
-
- - Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 6.0
- inference time (ms/im):
- - value: 80
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.7
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth
-
- - Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 7.3
- inference time (ms/im):
- - value: 100
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 44.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth
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- - Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 4.5
- inference time (ms/im):
- - value: 64.94
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.8
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.4
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth
-
- - Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 4.5
- 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: 41.5
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.1
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth
-
- - Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 6.5
- inference time (ms/im):
- - value: 85.47
- 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: 38.9
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth
-
- - Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 4.5
- inference time (ms/im):
- - value: 68.49
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 43.8
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth
-
- - Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 6.4
- inference time (ms/im):
- - value: 90.91
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 45.0
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth
-
- - Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 6.0
- inference time (ms/im):
- - value: 100
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 44.4
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 38.6
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth
-
- - Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 8.0
- inference time (ms/im):
- - value: 116.28
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 45.8
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 39.7
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth
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- - Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks
- Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
- Metadata:
- Training Memory (GB): 9.2
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 47.3
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 41.1
- Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth
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