Collections: - Name: Rethinking Classification and Localization for Object Detection Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - FPN - RPN - ResNet - RoIAlign Paper: URL: https://arxiv.org/pdf/1904.06493 Title: 'Rethinking Classification and Localization for Object Detection' README: configs/double_heads/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/roi_heads/double_roi_head.py#L6 Version: v2.0.0 Models: - Name: dh_faster_rcnn_r50_fpn_1x_coco In Collection: Rethinking Classification and Localization for Object Detection Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py Metadata: Training Memory (GB): 6.8 inference time (ms/im): - value: 105.26 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth