Architecture | TridentNet Block, ResNet, Soft-NMS |
---|---|
MS train | N |
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Architecture | TridentNet Block, ResNet, Soft-NMS |
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MS train | Y |
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Architecture | TridentNet Block, ResNet, Soft-NMS |
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MS train | Y |
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[ALGORITHM]
@InProceedings{li2019scale,
title={Scale-Aware Trident Networks for Object Detection},
author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
journal={The International Conference on Computer Vision (ICCV)},
year={2019}
}
We reports the test results using only one branch for inference.
Backbone | Style | mstrain | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |
---|---|---|---|---|---|---|---|
R-50 | caffe | N | 1x | 37.7 | model | log | ||
R-50 | caffe | Y | 1x | 37.6 | model | log | ||
R-50 | caffe | Y | 3x | 40.3 | model | log |
Note
Similar to Detectron2, we haven't implemented the Scale-aware Training Scheme in section 4.2 of the paper.
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
COCO minival | TridentNet (R-50, 3x, caffe, MS train=Y) | box AP | 40.3 | # 71 |
COCO minival | TridentNet (R-50, 1x, caffe, MS train=N) | box AP | 37.7 | # 97 |
COCO minival | TridentNet (R-50, 1x, caffe, MS train=Y) | box AP | 37.6 | # 98 |