Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, FPN, RoIAlign |
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lr sched | 20e |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, FPN, RoIAlign |
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lr sched | 20e |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, FPN, RoIAlign |
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lr sched | 20e |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | N |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | N |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | Y |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | N |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | N |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | Y |
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Architecture | Group Normalization, Non Maximum Suppression, FPN, HRNet |
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MS train | Y |
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Architecture | RPN, HRNet, Convolution, FPN, 1x1 Convolution, HTC, RoIAlign |
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lr sched | 20e |
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Architecture | RPN, HRNet, Convolution, FPN, 1x1 Convolution, HTC, RoIAlign |
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lr sched | 20e |
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Architecture | RPN, HRNet, Convolution, FPN, 1x1 Convolution, HTC, RoIAlign |
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lr sched | 20e |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 1x |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 2x |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 1x |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 2x |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 1x |
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Architecture | Softmax, RPN, HRNet, Convolution, Dense Connections, RoIAlign |
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lr sched | 2x |
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[ALGORITHM]
@inproceedings{SunXLW19,
title={Deep High-Resolution Representation Learning for Human Pose Estimation},
author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
booktitle={CVPR},
year={2019}
}
@article{SunZJCXLMWLW19,
title={High-Resolution Representations for Labeling Pixels and Regions},
author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao
and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
journal = {CoRR},
volume = {abs/1904.04514},
year={2019}
}
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | config | model | log |
HRNetV2p-W18 | pytorch | 2x | 6.6 | 38.9 | config | model | log | |
HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | config | model | log |
HRNetV2p-W32 | pytorch | 2x | 9.0 | 41.4 | config | model | log | |
HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | config | model | log |
HRNetV2p-W40 | pytorch | 2x | 10.4 | 42.1 | config | model | log |
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | config | model | log |
HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | config | model | log |
HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | config | model | log |
HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | config | model | log |
HRNetV2p-W40 | pytorch | 1x | 10.9 | 42.1 | 37.5 | config | model | log | |
HRNetV2p-W40 | pytorch | 2x | 10.9 | 42.8 | 38.2 | config | model | log |
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | config | model | log |
HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | config | model | log |
HRNetV2p-W40 | pytorch | 20e | 10.8 | 43.8 | config | model | log |
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 | 41.6 | 36.4 | config | model | log |
HRNetV2p-W32 | pytorch | 20e | 8.3 | 44.3 | 38.6 | config | model | log | |
HRNetV2p-W40 | pytorch | 20e | 12.5 | 45.1 | 39.3 | config | model | log |
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | config | model | log |
HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | config | model | log |
HRNetV2p-W40 | pytorch | 20e | 14.6 | 46.4 | 40.8 | config | model | log |
Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
---|---|---|---|---|---|---|---|---|---|
HRNetV2p-W18 | pytorch | Y | N | 1x | 13.0 | 12.9 | 35.3 | config | model | log |
HRNetV2p-W18 | pytorch | Y | N | 2x | 13.0 | - | 38.2 | config | model | log |
HRNetV2p-W32 | pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | config | model | log |
HRNetV2p-W32 | pytorch | Y | N | 2x | 17.5 | - | 40.8 | config | model | log |
HRNetV2p-W18 | pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | config | model | log |
HRNetV2p-W32 | pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | config | model | log |
HRNetV2p-W48 | pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | config | model | log |
Note:
28e
schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs.
MODEL | BOX AP |
---|---|
HTC (HRNetV2p-W40, 20e, pytorch) | 46.4 |
HTC (HRNetV2p-W32, 20e, pytorch) | 45.4 |
Cascade Mask R-CNN (HRNetV2p-W40, 20e, pytorch) | 45.1 |
Cascade Mask R-CNN (HRNetV2p-W32, 20e, pytorch) | 44.3 |
Cascade R-CNN (HRNetV2p-W40, 20e, pytorch) | 43.8 |
Cascade R-CNN (HRNetV2p-W32, 20e, pytorch) | 43.3 |
Mask R-CNN (HRNetV2p-W40, 2x, pytorch) | 42.8 |
HTC (HRNetV2p-W18, 20e, pytorch) | 42.8 |
FCOS (HRNetV2p-W48, 2x, pytorch, GN=Y, MS train=Y) | 42.7 |
Mask R-CNN (HRNetV2p-W32, 2x, pytorch) | 42.5 |
Faster R-CNN (HRNetV2p-W40, 2x, pytorch) | 42.1 |
Mask R-CNN (HRNetV2p-W40, 1x, pytorch) | 42.1 |
FCOS (HRNetV2p-W32, 2x, pytorch, GN=Y, MS train=Y) | 41.9 |
Cascade Mask R-CNN (HRNetV2p-W18, 20e, pytorch) | 41.6 |
Faster R-CNN (HRNetV2p-W32, 2x, pytorch) | 41.4 |
Cascade R-CNN (HRNetV2p-W18, 20e, pytorch) | 41.2 |
Faster R-CNN (HRNetV2p-W40, 1x, pytorch) | 41.2 |
Mask R-CNN (HRNetV2p-W32, 1x, pytorch) | 41.2 |
FCOS (HRNetV2p-W32, 2x, pytorch, GN=Y, MS train=N) | 40.8 |
Faster R-CNN (HRNetV2p-W32, 1x, pytorch) | 40.2 |
Mask R-CNN (HRNetV2p-W18, 2x, pytorch) | 39.8 |
FCOS (HRNetV2p-W32, 1x, pytorch, GN=Y, MS train=N) | 39.5 |
Faster R-CNN (HRNetV2p-W18, 2x, pytorch) | 38.9 |
FCOS (HRNetV2p-W18, 2x, pytorch, GN=Y, MS train=Y) | 38.3 |
FCOS (HRNetV2p-W18, 2x, pytorch, GN=Y, MS train=N) | 38.2 |
Mask R-CNN (HRNetV2p-W18, 1x, pytorch) | 37.7 |
Faster R-CNN (HRNetV2p-W18, 1x, pytorch) | 36.9 |
FCOS (HRNetV2p-W18, 1x, pytorch, GN=Y, MS train=N) | 35.3 |
MODEL | MASK AP |
---|---|
HTC (HRNetV2p-W40, 20e, pytorch) | 40.8 |
HTC (HRNetV2p-W32, 20e, pytorch) | 39.9 |
Cascade Mask R-CNN (HRNetV2p-W40, 20e, pytorch) | 39.3 |
Cascade Mask R-CNN (HRNetV2p-W32, 20e, pytorch) | 38.6 |
Mask R-CNN (HRNetV2p-W40, 2x, pytorch) | 38.2 |
HTC (HRNetV2p-W18, 20e, pytorch) | 37.9 |
Mask R-CNN (HRNetV2p-W32, 2x, pytorch) | 37.8 |
Mask R-CNN (HRNetV2p-W40, 1x, pytorch) | 37.5 |
Mask R-CNN (HRNetV2p-W32, 1x, pytorch) | 37.1 |
Cascade Mask R-CNN (HRNetV2p-W18, 20e, pytorch) | 36.4 |
Mask R-CNN (HRNetV2p-W18, 2x, pytorch) | 36.0 |
Mask R-CNN (HRNetV2p-W18, 1x, pytorch) | 34.2 |