Thank you for your contribution! Have you run these two models with dnn and record the inference speed? I would like to know which runs faster.

Have you run these two models with dnn and record the inference speed? I would like to know which runs faster

Average Inference Time for FlowNet v2: 0.83 seconds per frame
Average Inference Time for RAFT: 1.2 seconds per frame

This is how I calculated

start_time = cv.getTickCount()
flow = self.net.forward()
end_time = cv.getTickCount()
inference_time = (end_time - start_time) / cv.getTickFrequency()
self.total_time += inference_time

avg_time = opt_flow.total_time / num_frames

I used the model weights mentioned in the sample code file.

Have you run these two models with dnn and record the inference speed? I would like to know which runs faster

Average Inference Time for FlowNet v2: 0.83 seconds per frame Average Inference Time for RAFT: 1.2 seconds per frame

This is how I calculated

start_time = cv.getTickCount()
flow = self.net.forward()
end_time = cv.getTickCount()
inference_time = (end_time - start_time) / cv.getTickFrequency()
self.total_time += inference_time

avg_time = opt_flow.total_time / num_frames

I used the model weights mentioned in the sample code file.

That's useful information. Thank you!

@fengyuentau If there are no reviews can you approve these changes, so this PR can be merged?