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metafile.yml
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Collections:
- Name: SCNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- SCNet
Paper:
URL: https://arxiv.org/abs/2012.10150
Title: 'SCNet: Training Inference Sample Consistency for Instance Segmentation'
README: configs/scnet/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/scnet.py#L6
Version: v2.9.0
Models:
- Name: scnet_r50_fpn_1x_coco
In Collection: SCNet
Config: configs/scnet/scnet_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 161.29
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: 39.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth
- Name: scnet_r50_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_r50_fpn_20e_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 161.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco-a569f645.pth
- Name: scnet_r101_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_r101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 8.9
inference time (ms/im):
- value: 172.41
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco-294e312c.pth
- Name: scnet_x101_64x4d_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 13.2
inference time (ms/im):
- value: 204.08
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 42.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth