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- Collections:
- - Name: Deformable Convolutional Networks v2
- 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/dcnv2/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_mdconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks v2
- Config: configs/dcnv2/faster-rcnn_r50-mdconv-c3-c5_fpn_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 v2
- Config: configs/dcnv2/faster-rcnn_r50-mdconv-group4-c3-c5_fpn_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_mdpool_1x_coco
- In Collection: Deformable Convolutional Networks v2
- Config: configs/dcnv2/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: mask-rcnn_r50_fpn_mdconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks v2
- Config: configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_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_r50_fpn_fp16_mdconv_c3-c5_1x_coco
- In Collection: Deformable Convolutional Networks v2
- Config: configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_amp-1x_coco.py
- Metadata:
- Training Memory (GB): 3.1
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- - Mixed Precision Training
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 42.0
- - Task: Instance Segmentation
- Dataset: COCO
- Metrics:
- mask AP: 37.6
- Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth
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