文件列表:

LICENSE (11357, 2020-07-13)
cocoapi/ (0, 2020-07-13)
daec_exp/ (0, 2020-07-13)
daec_exp/compare_decoding_modes.py (2422, 2020-07-13)
daec_exp/extract_results.py (1227, 2020-07-13)
experiments/ (0, 2020-07-13)
experiments/coco/ (0, 2020-07-13)
experiments/coco/hourglass/ (0, 2020-07-13)
experiments/coco/hourglass/hg4_128x96_d256x3_adam_lr2.5e-4.yaml (1411, 2020-07-13)
experiments/coco/hourglass/hg4_256x192_d256x3_adam_lr2.5e-4.yaml (1412, 2020-07-13)
experiments/coco/hourglass/hg4_384x288_d256x3_adam_lr2.5e-4.yaml (1372, 2020-07-13)
experiments/coco/hourglass/hg8_128x96_d256x3_adam_lr2.5e-4.yaml (1411, 2020-07-13)
experiments/coco/hourglass/hg8_256x192_d256x3_adam_lr2.5e-4.yaml (1412, 2020-07-13)
experiments/coco/hourglass/hg8_384x288_d256x3_adam_lr2.5e-4.yaml (1413, 2020-07-13)
experiments/coco/hrnet/ (0, 2020-07-13)
experiments/coco/hrnet/w32_128x96_adam_lr1e-3.yaml (2154, 2020-07-13)
experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml (2182, 2020-07-13)
experiments/coco/hrnet/w32_384x288_adam_lr1e-3.yaml (2178, 2020-07-13)
experiments/coco/hrnet/w48_128x96_adam_lr1e-3.yaml (2222, 2020-07-13)
experiments/coco/hrnet/w48_256x192_adam_lr1e-3.yaml (2178, 2020-07-13)
experiments/coco/hrnet/w48_384x288_adam_lr1e-3.yaml (2178, 2020-07-13)
experiments/coco/resnet/ (0, 2020-07-13)
experiments/coco/resnet/res101_128x96_d256x3_adam_lr1e-3.yaml (1455, 2020-07-13)
experiments/coco/resnet/res101_256x192_d256x3_adam_lr1e-3.yaml (1519, 2020-07-13)
experiments/coco/resnet/res101_384x288_d256x3_adam_lr1e-3.yaml (1520, 2020-07-13)
experiments/coco/resnet/res152_128x96_d256x3_adam_lr1e-3.yaml (1455, 2020-07-13)
experiments/coco/resnet/res152_256x192_d256x3_adam_lr1e-3.yaml (1519, 2020-07-13)
experiments/coco/resnet/res152_384x288_d256x3_adam_lr1e-3.yaml (1520, 2020-07-13)
experiments/coco/resnet/res50_128x96_d256x3_adam_lr1e-3.yaml (1453, 2020-07-13)
experiments/coco/resnet/res50_256x192_d256x3_adam_lr1e-3.yaml (1517, 2020-07-13)
experiments/coco/resnet/res50_384x288_d256x3_adam_lr1e-3.yaml (1518, 2020-07-13)
experiments/mpii/ (0, 2020-07-13)
experiments/mpii/hrnet/ (0, 2020-07-13)
experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml (1962, 2020-07-13)
experiments/mpii/hrnet/w48_256x256_adam_lr1e-3.yaml (1962, 2020-07-13)
experiments/mpii/resnet/ (0, 2020-07-13)
experiments/mpii/resnet/res101_256x256_d256x3_adam_lr1e-3.yaml (1577, 2020-07-13)
experiments/mpii/resnet/res152_256x256_d256x3_adam_lr1e-3.yaml (1576, 2020-07-13)
... ...

# Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation

Serving as a model-agnostic plug-in, DAEC significantly improves the performance of a variety of state-of-the-art human pose estimation models!

## News * \[2020/07/13\] [Code](https://github.com/fyang235/DAEC/) is released. * \[2020/07/14\] DAEC is now on [ArXiv](https://arxiv.org/abs/2007.05887). ## Introduction     Serving as a model-agnostic plug-in, DAEC learns its decoding strategy from training data and remarkably improves the performance of a variety of state-of-the-art human pose estimation models. Extensive experiments performed on two common benchmarks, COCO and MPII, demonstrates that DAEC exceeds its competitors by considerable margins, backing up the rationality and generality of our novel heatmap decoding idea.

## Main Results ### Results on COCO val2017 | Model | Input | Method | AP | AP↑ | AP50 | AP75 | APM | APL | AR | AR50 | AR75 | ARM | ARL | | ----------- | ------- | -------- | ---------- | ------ | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | | ResNet\-50 | 256×192 | Standard | 65\.34 | 5\.29↑ | 90\.37 | 74\.48 | 63\.25 | 68\.59 | 69\.32 | 91\.85 | 77\.96 | 66\.57 | 73\.48 | | ResNet\-50 | 256×192 | Shifting | 66\.80 | 3\.83↑ | 90\.43 | 75\.74 | 65\.15 | 70\.28 | 70\.84 | 91\.99 | 78\.90 | 68\.09 | 75\.00 | | ResNet\-50 | 256×192 | DARK | 68\.40 | 2\.24↑ | 91\.38 | 76\.89 | 66\.60 | 71\.59 | 72\.01 | 92\.07 | 79\.72 | 69\.30 | 76\.14 | | ResNet\-50 | 256×192 | **DAEC** | **70\.63** | | **91\.40** | **78\.17** | **68\.27** | **74\.66** | **74\.11** | **92\.24** | **80\.81** | **70\.98** | **78\.85** | | ResNet\-50 | 384×288 | Standard | 69\.85 | 3\.07↑ | 91\.46 | 77\.07 | 66\.86 | 74\.66 | 73\.28 | 92\.48 | 79\.83 | 69\.55 | 78\.80 | | ResNet\-50 | 384×288 | Shifting | 70\.71 | 2\.21↑ | 91\.47 | 78\.01 | 67\.45 | 75\.55 | 73\.96 | 92\.51 | 80\.26 | 70\.18 | 79\.56 | | ResNet\-50 | 384×288 | DARK | 71\.49 | 1\.43↑ | 91\.47 | 78\.20 | 68\.43 | 76\.50 | 74\.71 | 92\.66 | 80\.79 | 70\.93 | 80\.35 | | ResNet\-50 | 384×288 | **DAEC** | **72\.92** | | **91\.52** | **79\.41** | **69\.20** | **78\.45** | **75\.80** | **92\.87** | **81\.72** | **71\.72** | **81\.86** | | ResNet\-101 | 256×192 | Standard | 66\.60 | 5\.38↑ | 91\.45 | 75\.77 | 65\.21 | 69\.60 | 70\.54 | 92\.46 | 78\.84 | 68\.04 | 74\.35 | | ResNet\-101 | 256×192 | Shifting | 68\.43 | 3\.55↑ | 91\.44 | 77\.89 | 66\.77 | 71\.40 | 72\.06 | 92\.44 | 80\.05 | 69\.60 | 75\.86 | | ResNet\-101 | 256×192 | DARK | 69\.30 | 2\.68↑ | 91\.48 | 78\.08 | 67\.85 | 72\.60 | 73\.13 | 92\.66 | 80\.72 | 70\.66 | 76\.99 | | ResNet\-101 | 256×192 | **DAEC** | **71\.98** | | **92\.48** | **79\.32** | **69\.60** | **75\.73** | **75\.31** | **93\.15** | **81\.85** | **72\.44** | **79\.73** | | ResNet\-101 | 384×288 | Standard | 71\.63 | 2\.89↑ | 92\.44 | 80\.19 | 69\.04 | 76\.02 | 75\.07 | 93\.25 | 82\.24 | 71\.75 | 80\.12 | | ResNet\-101 | 384×288 | Shifting | 72\.42 | 2\.10↑ | 92\.45 | 80\.25 | 69\.78 | 76\.66 | 75\.76 | 93\.26 | 82\.51 | 72\.49 | 80\.75 | | ResNet\-101 | 384×288 | DARK | 73\.22 | 1\.31↑ | 92\.47 | 80\.35 | 70\.70 | 77\.68 | 76\.51 | 93\.31 | 82\.97 | 73\.20 | 81\.56 | | ResNet\-101 | 384×288 | **DAEC** | **74\.52** | | **92\.47** | **81\.40** | **71\.44** | **79\.40** | **77\.55** | **93\.42** | **83\.61** | **73\.97** | **82\.99** | | ResNet\-152 | 256×192 | Standard | 67\.42 | 5\.34↑ | 91\.48 | 76\.75 | 65\.51 | 70\.85 | 71\.26 | 92\.66 | 79\.83 | 68\.63 | 75\.28 | | ResNet\-152 | 256×192 | Shifting | 68\.86 | 3\.90↑ | 91\.52 | 77\.86 | 67\.10 | 72\.23 | 72\.60 | 92\.85 | 80\.68 | 70\.02 | 76\.55 | | ResNet\-152 | 256×192 | DARK | 70\.17 | 2\.59↑ | 92\.47 | 78\.93 | 68\.17 | 73\.59 | 73\.74 | 93\.03 | 81\.27 | 71\.13 | 77\.77 | | ResNet\-152 | 256×192 | **DAEC** | **72\.75** | | **92\.51** | **80\.34** | **70\.00** | **76\.84** | **75\.95** | **93\.14** | **82\.68** | **72\.84** | **80\.68** | | ResNet\-152 | 384×288 | Standard | 72\.83 | 2\.65↑ | 92\.50 | 81\.38 | 70\.24 | 76\.99 | 76\.15 | 93\.64 | 83\.50 | 72\.95 | 81\.00 | | ResNet\-152 | 384×288 | Shifting | 73\.51 | 1\.98↑ | 92\.52 | 81\.47 | 70\.96 | 77\.74 | 76\.80 | 93\.73 | 83\.80 | 73\.60 | 81\.67 | | ResNet\-152 | 384×288 | DARK | 74\.26 | 1\.23↑ | **92\.54** | 82\.44 | 71\.88 | 78\.63 | 77\.50 | 93\.77 | 84\.32 | 74\.34 | 82\.31 | | ResNet\-152 | 384×288 | **DAEC** | **75\.48** | | **92\.54** | **82\.59** | **72\.57** | **80\.33** | **78\.50** | **93\.84** | **84\.70** | **75\.05** | **83\.75** | | HR\-W32 | 256×192 | Standard | 69\.66 | 5\.81↑ | 92\.49 | 79\.02 | 67\.87 | 73\.16 | 73\.42 | 93\.77 | 81\.99 | 70\.79 | 77\.48 | | HR\-W32 | 256×192 | Shifting | 71\.33 | 4\.13↑ | 92\.49 | 81\.11 | 69\.63 | 74\.68 | 74\.85 | 93\.78 | 83\.01 | 72\.21 | 78\.95 | | HR\-W32 | 256×192 | DARK | 72\.74 | 2\.73↑ | **92\.51** | 81\.41 | 70\.85 | 76\.57 | 76\.24 | 93\.83 | 83\.82 | 73\.46 | 80\.53 | | HR\-W32 | 256×192 | **DAEC** | **75\.47** | | 93\.49 | 83\.50 | 72\.86 | 79\.52 | 78\.35 | 94\.05 | 85\.11 | 75\.26 | 83\.13** | | HR\-W32 | 384×288 | Standard | 73\.53 | 3\.47↑ | 92\.54 | 82\.21 | 71\.24 | 77\.74 | 76\.94 | 93\.88 | 84\.15 | 73\.69 | 81\.92 | | HR\-W32 | 384×288 | Shifting | 74\.45 | 2\.55↑ | 92\.54 | 82\.33 | 71\.84 | 78\.62 | 77\.69 | 93\.92 | 84\.49 | 74\.45 | 82\.66 | | HR\-W32 | 384×288 | DARK | 75\.75 | 1\.25↑ | **93\.55** | 83\.33 | 73\.05 | 79\.92 | 78\.71 | 94\.16 | 85\.06 | 75\.45 | 83\.72 | | HR\-W32 | 384×288 | **DAEC** | **77\.00** | | 93\.54 | **83\.67** | **73\.86** | **81\.86** | **79\.71** | **94\.14** | **85\.64** | **76\.17** | **85\.13** | | HR\-W48 | 256×192 | Standard | 69\.86 | 5\.85↑ | 92\.48 | 79\.79 | 68\.12 | 73\.31 | 73\.70 | 93\.73 | 82\.31 | 70\.90 | 77\.92 | | HR\-W48 | 256×192 | Shifting | 71\.53 | 4\.17↑ | 92\.50 | 81\.03 | 69\.56 | 75\.05 | 75\.23 | 93\.78 | 83\.28 | 72\.38 | 79\.55 | | HR\-W48 | 256×192 | DARK | 72\.84 | 2\.86↑ | 92\.52 | 82\.11 | 71\.18 | 76\.36 | 76\.51 | 93\.86 | 84\.18 | 73\.70 | 80\.81 | | HR\-W48 | 256×192 | **DAEC** | **75\.70** | | **93\.50** | **83\.56** | **73\.05** | **79\.92** | **78\.71** | **94\.07** | **85\.53** | **75\.44** | **83\.68** | | HR\-W48 | 384×288 | Standard | 74\.42 | 2\.82↑ | 93\.48 | 82\.41 | 71\.72 | 78\.60 | 77\.60 | 94\.05 | 84\.65 | 74\.41 | 82\.49 | | HR\-W48 | 384×288 | Shifting | 75\.18 | 2\.05↑ | 93\.48 | 82\.53 | 72\.54 | 79\.39 | 78\.28 | 94\.11 | 84\.93 | 75\.11 | 83\.16 | | HR\-W48 | 384×288 | DARK | 76\.15 | 1\.08↑ | 93\.50 | 83\.69 | 73\.59 | 80\.46 | 79\.15 | 94\.11 | 85\.67 | 75\.99 | 84\.02 | | HR\-W48 | 384×288 | **DAEC** | **77\.23** | | **93\.52** | **83\.74** | **74\.15** | **82\.25** | **80\.07** | **94\.24** | **85\.97** | **76\.61** | **85\.41** | #### Note: - Flip test is not used. ### Results on MPII validation | Model | Method | Head | Shoul\. | Elbow | Wrist | Hip | Knee | Ankle | PCKh0\.1 | ↑ | PCKh0\.5 | ↑ | | ----------- | -------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ------- | ---------- | ------ | | ResNet\-50 | Standard | 96\.04 | 94\.19 | 87\.25 | 81\.34 | 86\.15 | 81\.60 | 78\.32 | 21\.55 | 10\.13↑ | 86\.99 | 0\.95↑ | | ResNet\-50 | Shifting | 96\.04 | 94\.34 | 87\.35 | 81\.53 | 86\.41 | 81\.85 | 78\.48 | 23\.40 | 8\.28↑ | 87\.15 | 0\.79↑ | | ResNet\-50 | DARK | **96\.15** | 94\.53 | 87\.76 | 81\.87 | 86\.76 | 82\.49 | 78\.81 | 24\.48 | 7\.20↑ | 87\.48 | 0\.47↑ | | ResNet\-50 | **DAEC** | 95\.87 | **94\.87** | **88\.44** | **82\.05** | **87\.62** | **83\.22** | **79\.48** | **31\.69** | | **87\.95** | | | ResNet\-101 | Standard | 96\.35 | 94\.62 | 87\.40 | 82\.41 | 85\.72 | 82\.35 | 78\.77 | 22\.07 | 10\.02↑ | 87\.36 | 0\.89↑ | | ResNet\-101 | Shifting | 96\.59 | 94\.58 | 87\.69 | 82\.39 | 86\.22 | 82\.71 | 78\.98 | 23\.66 | 8\.43↑ | 87\.56 | 0\.69↑ | | ResNet\-101 | DARK | **96\.32** | 94\.72 | 88\.07 | 82\.85 | 86\.71 | 83\.16 | 79\.24 | 24\.82 | 7\.27↑ | 87\.85 | 0\.39↑ | | ResNet\-101 | **DAEC** | 96\.28 | **94\.80** | **88\.55** | **83\.42** | **87\.54** | **83\.42** | **79\.74** | **32\.09** | | **88\.25** | | | ResNet\-152 | Standard | 96\.62 | 95\.02 | 88\.27 | 82\.70 | 86\.38 | 83\.30 | 79\.85 | 22\.55 | 10\.52↑ | 87\.98 | 0\.80↑ | | ResNet\-152 | Shifting | 96\.62 | 95\.31 | 88\.56 | 82\.99 | 86\.91 | 83\.58 | 79\.83 | 24\.31 | 8\.76↑ | 88\.23 | 0\.56↑ | | ResNet\-152 | DARK | **96\.73** | 95\.33 | 88\.80 | 83\.66 | 87\.02 | 83\.78 | **80\.63** | 25\.28 | 7\.79↑ | 88\.50 | 0\.28↑ | | ResNet\-152 | **DAEC** | 96\.56 | **95\.67** | **88\.97** | **83\.85** | **87\.99** | **84\.14** | 80\.52 | **33\.07** | | **88\.78** | | | HR\-W32 | Standard | 96\.79 | 95\.06 | 89\.08 | 84\.29 | 86\.01 | 84\.40 | 81\.39 | 23\.49 | 12\.31↑ | 88\.61 | 1\.06↑ | | HR\-W32 | Shifting | 96\.93 | 95\.25 | 89\.06 | 84\.39 | 86\.43 | 84\.89 | 81\.58 | 25\.36 | 10\.44↑ | 88\.81 | 0\.86↑ | | HR\-W32 | DARK | **96\.97** | 95\.40 | 89\.57 | 85\.03 | 87\.04 | 85\.67 | 82\.03 | 27\.38 | 8\.42↑ | 89\.25 | 0\.41↑ | | HR\-W32 | **DAEC** | 96\.86 | **95\.58** | **89\.98** | **85\.49** | **87\.83** | **86\.18** | **82\.59** | **35\.80** | | **89\.67** | | #### Note: - Flip test is not used. ### Speed Comparison | Method | Shifting | DARK | DAEC | | ------------ | ------------- | ------------- | ------------- | | Elapsed time | 0.31 ms/image | 3.00 ms/image | 1.44 ms/image | #### Note: - Tested with HR\-W32-256×192 using Intel Core i7-9700F CPU - Values are extra time cost compared with the standard decoding ## Get Started This project is created on the basis of the [DARK](https://github.com/ilovepose/DarkPose) and [HRNet](https://github.com/HRNet/HRNet-Human-Pose-Estimation) projects. Refer to these two projects to get your datasets and models ready. To reproduce our results, run: ``` python daec_exp/compare_decoding_modes.py > daec_exp/results/results.txt 2>&1 ``` followed by: ``` python daec_exp/extract_results.py ``` then, the results are listed in file : ``` daec_exp/results/results_slim.txt ``` ### Citation If you use our code or models in your research, please cite with: ``` @InProceedings{ author = {Feiyu Yang, Yu Chen, Zhe Pan, Min Zhang, Min Xue, Yaoyang Mo, Yao Zhang, Guoxiong Guan, Beibei Qian, Zhenzhong Xiao, Zhan Song}, title = {Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation}, month = {July}, year = {2020} } ```

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：