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- Collections:
- - Name: NAS-FPN
- Metadata:
- Training Data: COCO
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- Training Resources: 8x V100 GPUs
- Architecture:
- - NAS-FPN
- - ResNet
- Paper:
- URL: https://arxiv.org/abs/1904.07392
- Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection'
- README: configs/nas_fpn/README.md
- Code:
- URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67
- Version: v2.0.0
- Models:
- - Name: retinanet_r50_fpn_crop640-50e_coco
- In Collection: NAS-FPN
- Config: configs/nas_fpn/retinanet_r50_fpn_crop640-50e_coco.py
- Metadata:
- Training Memory (GB): 12.9
- inference time (ms/im):
- - value: 43.67
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 50
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 37.9
- Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth
- - Name: retinanet_r50_nasfpn_crop640-50e_coco
- In Collection: NAS-FPN
- Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py
- Metadata:
- Training Memory (GB): 13.2
- inference time (ms/im):
- - value: 43.48
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 50
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth
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