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

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.

• 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

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

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

2. onnx转TensorRT

python demo_tensorrt.py

3. 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}
}