Finally, it’s possible to perform YoloV3 inference using pb weights: great!
it works, but some behaviours are obscure.
using getUnconnectedOutLayersNames() or getLayerNames() or dumpToFile, the output layers seems to be:
conv2d_59/convolution, conv2d_67/convolution, conv2d_75/convolution
It seems that OpenCV doesn’t parse the bias operation, that should be performed by last layers of the network:
conv2d_59/BiasAdd, conv2d_67/BiasAdd, conv2d_75/BiasAdd
The problem is that OpenCV::dnn module performs correctly the bias operation even if the BiasAdd layers seems disappeared.
This is verified by comparing with the inference performed using tensorflow on the same pb weights and the same image. The results are the same.
In conclusion: OpenCV 4.5.3 doesn’t list the Bias layers but applies the biases correctly.
info: YoloV3 trained on COCO dataset, “yolo.pb” weights, OpenCV 4.5.3 compiled with CUDA 10.0, C++, Windows 10.