I am trying to test the Object Detection Python demo.
python3 open_model_zoo/demos/object_detection_demo/python/object_detection_demo.py -d CPU -i /home/user/Downloads/car-detection.mp4 -m /home/user/intel/openvino_2021.4.752/deployment_tools/open_model_zoo/models/public/yolo-v3-tf/yolo-v3.xml -at yolo -o output_%03d.jpg
I downloaded the model via model downloader:
./downloader.py --name yolo-v3-tf
and converted it with model optimizer:
python3 ./model_optimizer/mo.py --input_model /home/user/intel/openvino_2021.4.752/deployment_tools/open_model_zoo/models/public/yolo-v3-tf/yolo-v3.pb --input_shape "[1, 416, 416, 3]" --reverse_input_channels
The results are nowhere near like in the video from the link:
I attached two random output frames.
What am I doing wrong?
I tried the OpenVINO Object Detection Demo with this video sample.
I converted the model using converter.py with the default shape since I didn't provide any input_shape parameter.
From my side it seems to work properly:
This is the full inferencing command that I used:
I recommend using the latest version of OpenVINO if it's feasible for you since I noticed you are using version 2021.4.
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