YOLOX-OpenVINO in Python

This toturial includes a Python demo for OpenVINO, as well as some converted models.

Model	Parameters	GFLOPs	Test Size	mAP	Weights
YOLOX-Nano	0.91M	1.08	416x416	25.3	onedrive/github
YOLOX-Tiny	5.06M	6.45	416x416	31.7	onedrive/github
YOLOX-S	9.0M	26.8	640x640	39.6	onedrive/github
YOLOX-M	25.3M	73.8	640x640	46.4	onedrive/github
YOLOX-L	54.2M	155.6	640x640	50.0	onedrive/github
YOLOX-Darknet53	63.72M	185.3	640x640	47.3	onedrive/github
YOLOX-X	99.1M	281.9	640x640	51.2	onedrive/github

Install OpenVINO Toolkit

Please visit Openvino Homepage for more details.

Option1. Set up the environment tempororally. You need to run this command everytime you start a new shell window.

source /opt/intel/openvino_2021/bin/setupvars.sh

Option2. Set up the environment permenantly.

Step1. For Linux:

vim ~/.bashrc

Step2. Add the following line into your file:

source /opt/intel/openvino_2021/bin/setupvars.sh

Step3. Save and exit the file, then run:

source ~/.bashrc

Export ONNX model

Please refer to the ONNX toturial. Note that you should set --opset to 10, otherwise your next step will fail.

Convert ONNX to OpenVINO

cd <INSTSLL_DIR>/openvino_2021/deployment_tools/model_optimizer

Install requirements for convert tool

sudo ./install_prerequisites/install_prerequisites_onnx.sh

Then convert model.

python3 mo.py --input_model <ONNX_MODEL> --input_shape <INPUT_SHAPE> [--data_type FP16]

For example:

python3 mo.py --input_model yolox.onnx --input_shape [1,3,640,640] --data_type FP16 --output_dir converted_output

python openvino_inference.py -m <XML_MODEL_PATH> -i <IMAGE_PATH>

or

python openvino_inference.py -m <XML_MODEL_PATH> -i <IMAGE_PATH> -o <OUTPUT_DIR> -s <SCORE_THR> -d <DEVICE>