| Collections: | |
| - Name: AutoAssign | |
| Metadata: | |
| Training Data: COCO | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| Training Resources: 8x V100 GPUs | |
| Architecture: | |
| - AutoAssign | |
| - FPN | |
| - ResNet | |
| Paper: | |
| URL: https://arxiv.org/abs/2007.03496 | |
| Title: 'AutoAssign: Differentiable Label Assignment for Dense Object Detection' | |
| README: configs/autoassign/README.md | |
| Code: | |
| URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/autoassign.py#L6 | |
| Version: v2.12.0 | |
| Models: | |
| - Name: autoassign_r50_fpn_8x2_1x_coco | |
| In Collection: AutoAssign | |
| Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py | |
| Metadata: | |
| Training Memory (GB): 4.08 | |
| Epochs: 12 | |
| Results: | |
| - Task: Object Detection | |
| Dataset: COCO | |
| Metrics: | |
| box AP: 40.4 | |
| Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth | |