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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
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