124 lines
4.1 KiB
YAML
124 lines
4.1 KiB
YAML
Collections:
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- Name: Deformable Convolutional Networks v2
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Metadata:
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Training Data: COCO
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Architecture:
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- Deformable Convolution
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Paper:
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URL: https://arxiv.org/abs/1811.11168
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Title: "Deformable ConvNets v2: More Deformable, Better Results"
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README: configs/dcnv2/README.md
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Code:
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15
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Version: v2.0.0
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Models:
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- Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
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In Collection: Deformable Convolutional Networks v2
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Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
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Metadata:
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Training Memory (GB): 4.1
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inference time (ms/im):
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- value: 56.82
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (800, 1333)
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Epochs: 12
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 41.4
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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
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- Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco
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In Collection: Deformable Convolutional Networks v2
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Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
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Metadata:
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Training Memory (GB): 4.2
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inference time (ms/im):
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- value: 57.47
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (800, 1333)
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Epochs: 12
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 41.5
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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
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- Name: faster_rcnn_r50_fpn_mdpool_1x_coco
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In Collection: Deformable Convolutional Networks v2
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Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
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Metadata:
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Training Memory (GB): 5.8
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inference time (ms/im):
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- value: 60.24
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (800, 1333)
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Epochs: 12
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 38.7
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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
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- Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
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In Collection: Deformable Convolutional Networks v2
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Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
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Metadata:
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Training Memory (GB): 4.5
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inference time (ms/im):
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- value: 66.23
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (800, 1333)
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Epochs: 12
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 41.5
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- Task: Instance Segmentation
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Dataset: COCO
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Metrics:
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mask AP: 37.1
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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
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- Name: mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco
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In Collection: Deformable Convolutional Networks v2
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Config: configs/dcn/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
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Metadata:
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Training Memory (GB): 3.1
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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- Mixed Precision Training
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Epochs: 12
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 42.0
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- Task: Instance Segmentation
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Dataset: COCO
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Metrics:
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mask AP: 37.6
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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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