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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
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