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/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 v2 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_mdpool_1x_coco In Collection: Deformable Convolutional Networks v2 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: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco In Collection: Deformable Convolutional Networks v2 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_r50_fpn_fp16_mdconv_c3-c5_1x_coco In Collection: Deformable Convolutional Networks v2 Config: configs/dcn/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_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