| 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/1703.06211 | |
| Title: "Deformable Convolutional Networks" | |
| 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-dconv-c3-c5_fpn_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_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_r101-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/faster-rcnn_r101-dconv-c3-c5_fpn_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-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_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 | |
| - Name: mask-rcnn_r50_fpn_dconv_c3-c5_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_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_fp16_dconv_c3-c5_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_amp-1x_coco.py | |
| Metadata: | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| - Mixed Precision Training | |
| Training Memory (GB): 3.0 | |
| Epochs: 12 | |
| Results: | |
| - Task: Object Detection | |
| Dataset: COCO | |
| Metrics: | |
| box AP: 41.9 | |
| - Task: Instance Segmentation | |
| Dataset: COCO | |
| Metrics: | |
| mask AP: 37.5 | |
| Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth | |
| - Name: mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/mask-rcnn_r101-dconv-c3-c5_fpn_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-dconv-c3-c5_fpn_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-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/cascade-rcnn_r101-dconv-c3-c5_fpn_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-dconv-c3-c5_fpn_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-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/cascade-mask-rcnn_r101-dconv-c3-c5_fpn_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 | |
| - Name: cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco | |
| In Collection: Deformable Convolutional Networks | |
| Config: configs/dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_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 | |