Comments (2)
Processing asynchronously will shift the frame position slightly, but it will be faster.
If you process in synchronization, the frame will not shift, but it will be slightly slower.
Use "openvino_yolov3_test.py" or "openvino_tiny-yolov3_test.py" if you want to use synchronous processing.
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@PINTO0309 thanks for responding so quickly. I just queried the inputs in the inference request to get the right correlation.
On a separate note, how much latency do you observe on running inference per image on the Myriad? Even though the throughput increases with the multiple sticks implementation, the latency for inference will be the same. Do you have any tips on how to speed up the inference?
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Related Issues (20)
- Cannot get results from pb model.
- train
- Darknet19 pre-train model convert error.
- Need help in converting the results into audio output HOT 2
- What is the output mean? HOT 1
- FPS HOT 4
- Yolov3_tiny python mo_tf.py run error
- Openvino performance a lot worse than darknet model HOT 3
- Decoding format of results stored in output blobs
- kvs flask pipeline and async_lock issue on multiple request
- Yolov4 HOT 2
- Not compatible with 2020.4 since the file libcpu_extension.so is missing HOT 2
- Exception:Path to the model tiny-yolo-v3 doesn't exist or it's a directory HOT 6
- IndexError: tuple index out of range HOT 1
- RuntimeError: Can not init Myriad device: NC_ERROR
- Argument shapes are inconsistent; they must have the same rank, and must have equal dimension everywhere except on the concatenation axis (axis 1).
- cnt, dev = heapq.heappop(self.heap_request) IndexError: index out of range
- Rectangle only on the center of the object when running YOLOv3-Tiny on NCS2 with smaller size HOT 2
- In linux, compile darknet as libdarknet.so. I just write a simple yolo_console_dll.cpp to link .so.
- The download scripts for lrmodels fails. HOT 1
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