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YOLO-Object-Detection optimization on Xeon scalable processors

In this project, optimization of TensorFlow code is performed for an object detection application to obtain real-time performance.
Please refer the following paper for all the details regarding performance optimizations,


Python 2.7

Steps to use this code:

  1. Go to utils/ and run:
    $ python
    this downloads the darknet weight files. Also, fuses batchnorm layers and creates TensorFlow Ckpt files.

  2. To run image inference:
    $ python , to run TinyYolo model
    $ python --image= [image path]
    $ python --v2 , to run YoloV2 model
    $ NUM_INTER_THREADS=2 NUM_INTRA_THREADS=8 python --par , to run parallel TensorFlow session(Inter/Intra op threads), if it is supported in your system.

  3. To run Webcam inference:
    $ python

Please refer the paper mentioned above to know more about the system used for testing and the versions of software tools used.