The tensors with the same first two dimensions all refer to features that are derived from one specific head. For example, every tensor with 20x20 as the first two dimensions are derived from the P5 head of YOLO, 40x40 is the P4 head and 80x80 is the P3 head.
Every tensor whose last dimension is 80 is used for classification. This corresponds to the 80 classes in COCO. Every tensor whose last dimension is 64 is used for bounding box regression. Every tensor whose last dimension is 32 (except the tensor with shape 160x160x32) are the mask coefficients.
The last tensor which has the largest two dimensions (160x160x32) is the proto tensor.
For bounding box and classification if you follow from this line you can see how it is decoded: ultralytics/ultralytics/nn/modules/head.py at main · ultralytics/ultralytics · GitHub
Here is how masks are decoded: ultralytics/ultralytics/utils/ops.py at main · ultralytics/ultralytics · GitHub
Alternatively, if you use DeGirum PySDK we include a C++ postprocessor that already handles segmentation results. You can get the speed of C++ postprocessing with the ease of a python interface. You can see details here for compiling a segmentation model Early Access to DeGirum Cloud Compiler