fire717
commented
2 years ago 我当时复现的时候好像还没有multipose，后面也没有关注了。
大概看了下hub的说明 `Outputs A float32 tensor of shape [1, 6, 56].
The first dimension is the batch dimension, which is always equal to 1. The second dimension corresponds to the maximum number of instance detections. The model can detect up to 6 people in the image frame simultaneously. The third dimension represents the predicted bounding box/keypoint locations and scores. The first 17 * 3 elements are the keypoint locations and scores in the format: [y_0, x_0, s_0, y_1, x_1, s_1, …, y_16, x_16, s_16], where y_i, x_i, s_i are the yx-coordinates (normalized to image frame, e.g. range in [0.0, 1.0]) and confidence scores of the i-th joint correspondingly. The order of the 17 keypoint joints is: [nose, left eye, right eye, left ear, right ear, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, right ankle]. The remaining 5 elements [ymin, xmin, ymax, xmax, score] represent the region of the bounding box (in normalized coordinates) and the confidence score of the instance.`
我猜测应该跟singlepose一样的，而它支持最多6人，因此对于singlepose修改应该就是取一个中心的heatmap变成取六个中心的，其余操作应该类似，根据这6个中心去找对应的关键点进行分组。
因为任务复杂了，所以模型的输入尺寸、 depth multiplier都相应增大了。
liamsun2019
LIUTAOGE
bigliuyq
fire老兄，请问你了解multipose的后处理吗？通过观察官方的multipose模型，看起来后处理跟singlepose颇有不同，不过有一些细节还不是很明白。