104 lines
3.5 KiB
YAML
104 lines
3.5 KiB
YAML
Collections:
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- Name: Empirical Attention
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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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- FPN
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- RPN
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- ResNet
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- RoIAlign
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- Spatial Attention
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Paper:
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URL: https://arxiv.org/pdf/1904.05873
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Title: 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
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README: configs/empirical_attention/README.md
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Code:
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/generalized_attention.py#L10
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Version: v2.0.0
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Models:
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- Name: faster_rcnn_r50_fpn_attention_1111_1x_coco
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In Collection: Empirical Attention
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Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
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Metadata:
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Training Memory (GB): 8.0
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inference time (ms/im):
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- value: 72.46
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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: 40.0
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Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth
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- Name: faster_rcnn_r50_fpn_attention_0010_1x_coco
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In Collection: Empirical Attention
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Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_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: 54.35
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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: 39.1
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Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth
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- Name: faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco
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In Collection: Empirical Attention
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Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
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Metadata:
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Training Memory (GB): 8.0
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inference time (ms/im):
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- value: 78.74
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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: 42.1
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Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth
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- Name: faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco
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In Collection: Empirical Attention
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Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_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: 58.48
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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: 42.0
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Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth
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