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_ms-2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r50_fpn_ms-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_ms-2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r101_fpn_ms-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-dconv-c3-c5_fpn_ms-2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-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_ms-2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_x101-32x4d_fpn_ms-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-dconv-c4-c5_fpn_ms-2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_x101-32x4d-dconv-c4-c5_fpn_ms-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