Add YOLOv5Face model support #38
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DefTruth
commented
Jul 25, 2022
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DefTruth
commented
- add YOLOv5Face model support
- add FaceDetectionResult、Sort/NMS/Visualize methods for FaceDetectionResult
- format ScaledYOLOv4
- fixed runtime_option print func bugs
merge from paddlepaddle/fastdeploy:develop
jiangjiajun
approved these changes
Jul 25, 2022
| std::vector<std::array<float, 4>> boxes; | ||
| // landmark: x, y, landmarks may empty if the | ||
| // model don't detect face with landmarks. | ||
| // Note, one face might have multiple landmarks, |
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什么类型的模型会没有landmark； 当没有landmark时，模型的开发应该是用DetectionResullt，还是用FaceDetectionResult呢
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- 关于什么时候会没有landmark？
在人脸检测中，带landmark和不带landmark的模型都是非常常见的。landmark通常作为一个辅助任务出现在人脸检测的算法中。所以定义一个FaceDetectionResult确实是需要考虑这两种情况。以下是一些例子：
- 带landmark的人脸检测器：YOLOv5Face、RetinaFace、SCRFD、CenterFace、BlazeFace等等
- 不带landmark的人脸检测器：DSFD(Tencent)、SSH、UltraFace、LFFD、LFD、S3FD等等
- 关于若不带landmark是否复用DetectionResullt？
个人观点：不建议。理由有以下3个：
- 首先，人脸检测和通用目标检测确实应该是两个不同的cv子领域，虽然说有一定的相似性，但从任务上来说是不一样的，都用DetectionResullt感觉有点奇怪
- 人脸检测没有多分类的label_id，只有人脸，目标检测通常是多类别的，需要有label_id；因此人脸检测也不需要像yolov5那样在做NMS时先通过label_id*max_wh来做offset提高recall
- 很多的人脸检测器带有landmark，但是大部分目标检测器是不带landmark的
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