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metafile.yml
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Collections:
- Name: Generalized Focal Loss
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Generalized Focal Loss
- FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/2006.04388
Title: 'Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection'
README: configs/gfl/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/gfl.py#L6
Version: v2.2.0
Models:
- Name: gfl_r50_fpn_1x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_r50_fpn_1x_coco.py
Metadata:
inference time (ms/im):
- value: 51.28
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth
- Name: gfl_r50_fpn_mstrain_2x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
Metadata:
inference time (ms/im):
- value: 51.28
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth
- Name: gfl_r101_fpn_mstrain_2x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
Metadata:
inference time (ms/im):
- value: 68.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth
- Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
Metadata:
inference time (ms/im):
- value: 77.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth
- Name: gfl_x101_32x4d_fpn_mstrain_2x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
Metadata:
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth
- Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco
In Collection: Generalized Focal Loss
Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
Metadata:
inference time (ms/im):
- value: 93.46
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 48.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth