| Collections: | |
| - Name: DDOD | |
| Metadata: | |
| Training Data: COCO | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| Training Resources: 8x V100 GPUs | |
| Architecture: | |
| - DDOD | |
| - FPN | |
| - ResNet | |
| Paper: | |
| URL: https://arxiv.org/pdf/2107.02963.pdf | |
| Title: 'Disentangle Your Dense Object Detector' | |
| README: configs/ddod/README.md | |
| Code: | |
| URL: https://github.com/open-mmlab/mmdetection/blob/v2.25.0/mmdet/models/detectors/ddod.py#L6 | |
| Version: v2.25.0 | |
| Models: | |
| - Name: ddod_r50_fpn_1x_coco | |
| In Collection: DDOD | |
| Config: configs/ddod/ddod_r50_fpn_1x_coco.py | |
| Metadata: | |
| Training Memory (GB): 3.4 | |
| Epochs: 12 | |
| Results: | |
| - Task: Object Detection | |
| Dataset: COCO | |
| Metrics: | |
| box AP: 41.7 | |
| Weights: https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth | |