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基于CenterNet训练的目标检测&人脸对齐&姿态估计模型

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CenterMulti代码实现参考以下

Object detection, 3D detection, and pose estimation using center point detection:

Objects as Points,
Xingyi Zhou, Dequan Wang, Philipp Krähenbühl,

2021.03.16:centernet2横空出世

COCO

Model COCO val mAP FPS
CenterNet-S4_DLA_8x 42.5 71
CenterNet2_R50_1x 42.9 24
CenterNet2_X101-DCN_2x 49.9 8
CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST 56.1 5
CenterNet2_DLA-BiFPN-P5_24x_ST 49.2 38

2020.04.09:基于centernet的the-state-of-the-art目标跟踪方法

tracker

2020.03.25: > 更强大的centernet优化版本,resnet50+without DCN+mAP=35.7(3.1% )↑

2019.06.10: > CenterNet code

1. Backebone

  • Strong: 增加支持mobilenetV2,mobilenetV3,efficientdet,shufflenetv2,部分网络需要支持DCNv2.

performance

  • Data process: 添加widerface转coco格式,参见root/data_process.

2. Purpose

2.1 Object Detection

  • 类别: 可支持行人、人脸、车辆、缺陷等检测,只需要修改数据加载即可
Backbone AP / FPS Flip AP / FPS Multi-scale AP / FPS
Hourglass-104 40.3 / 14 42.2 / 7.8 45.1 / 1.4
DLA-34 37.4 / 52 39.2 / 28 41.7 / 4
ResNet-101 34.6 / 45 36.2 / 25 39.3 / 4
ResNet-18 28.1 / 142 30.0 / 71 33.2 / 12

All models and details are available in > CenterNet MODEL_ZOO

shoulder defect

2.2 keypoint or pose

  • 姿态估计or关键点检测: 修改keypoint的数量及coco加载keypoint的格式可针对性训练多种形式的pose(如landmark等)

姿态估计参考centerpose

Backbone AP FPS TensorRT Speed Download
DLA-34 62.7 23 - model
Resnet-50 54.5 28 33 model
MobilenetV3 46.0 30 50 model
ShuffleNetV2 43.9 25 - model
High Resolution 57.1 16 - model
HardNet 45.6 30 - model
Darknet53 34.2 30 - model

face_landmark

2.3 model

centerface/shoulder/defect模型 提取码: u3pq

  • defect: defect模型基于mobilenetv2训练,由于部分数据标定不准,所以结果会有偏差,建议只供pre-train.

  • centerface: 该版本的centerface是基于修改的centernet训练,训练数据参照widerface,其中对质量不好的face做了过滤,使其更适合人脸识别的工程应用,模型有两个,分别是3.5M和8.9M.

centerface的训练:例如修改lib/datasets/coco_hp.py里num_joints = 5;flip_idx = [[0, 1], [3, 4]]以及整个项目里17的关节点数全部置换成5,dets[39:51]这类全部换成dets[15:20]等

3. TensorRT

  1. torch转onnx
python convert2onnx.py
  1. onnx转TensorRT
python demo_tensorrt.py
  1. 检测框架支持的TensorRT

TensorRT C++

    #shoulder检测模型支持该框架加速(不需要DCNs),total runtime = 3.82147 ms
    #在include/ctdetConfig.h里添加以下,然后cmake即可
    constexpr static int input_w = 512 ;
    constexpr static int input_h = 512 ;
    constexpr static int channel = 3 ;
    constexpr static int classNum = 1 ;
    constexpr static float mean[]= {0.408, 0.447, 0.470};
    constexpr static float std[] = {0.289, 0.274, 0.278};
    constexpr static char *className[]= {(char*)"shoulder"};

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@contact{yangsai1991@163.com,
  title={Objects as Points},
  author={bleakie},
  year={2019}
}

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