| Collections: | |
| - Name: Dynamic R-CNN | |
| Metadata: | |
| Training Data: COCO | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| Training Resources: 8x V100 GPUs | |
| Architecture: | |
| - Dynamic R-CNN | |
| - FPN | |
| - RPN | |
| - ResNet | |
| - RoIAlign | |
| Paper: | |
| URL: https://arxiv.org/pdf/2004.06002 | |
| Title: 'Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training' | |
| README: configs/dynamic_rcnn/README.md | |
| Code: | |
| URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/roi_heads/dynamic_roi_head.py#L11 | |
| Version: v2.2.0 | |
| Models: | |
| - Name: dynamic-rcnn_r50_fpn_1x_coco | |
| In Collection: Dynamic R-CNN | |
| Config: configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py | |
| Metadata: | |
| Training Memory (GB): 3.8 | |
| Epochs: 12 | |
| Results: | |
| - Task: Object Detection | |
| Dataset: COCO | |
| Metrics: | |
| box AP: 38.9 | |
| Weights: https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth | |