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I recently converted my trained model (it's Tiny YOLO v2 via Darkflow transformation, has only one class to detect) to NCS graph, and modified @Tome_at_Intel 's example code to run the test using my webcam.
Before doing this, I have tried the sample of live-object-detector with ncapi2_shim (I'm using NCSDK2). The result was fine. The script was processing the test video at the speed of about 80 ms per frame.
But when I applied my own Tiny YOLO v2, the processing time jump to about 1200 ms per frame.
The processing/inference time was obtained by using the following code (it's copied and slightly modified from the code of live-object-detector):
inference_time = graph.get_option(mvnc.GraphOption.RO_TIME_TAKEN)
To confirm the result, I went back to follow @Tome_at_Intel 's comment in which he provided step-by-step instruction of how to generate the frozen model (and it's actually what I've done with my own trained model, of course with different cfg and weight files...) for
The compile command I used was:
mvNCCompile -in input -on output built_graph/tiny-yolo-v2.pb
I used the converted NCS graph (with 20 classes to detect) with the dog.jpg as the input, and the inference time was still about 1200 ms. The output I got is listed as the following:
Found objects in 1207.973388671875 ms. dog 97.35302828411412 222.60164861726685 343.3950671757223 531.8106313107114 car 444.2567046299852 90.19346571245428 685.6577025125912 185.98194001005976 bicycle 139.43421012548873 144.2388337194188 599.3307892196749 434.39073473429 car 452.1365970362832 71.05730864847379 545.5606719185531 152.82092145965296
I always expect Tiny YOLO should be faster than SSD, but the results show that it's not. It is not only slower, but far far slower than the inference time shown in the live-object-detector sample case.
Did I do something stupid in the test or miss anything which is essential to apply Darkflow's Tiny YOLO v2 to NCS? Or there's something with NCS hardware/software in which we users cannot do anything?
Any suggestion or information will be helpful. Thanks.