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PyQT5 based GUI application for performance comparison between Tensorflow (CPU) and PlaidML (GPU)

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PlaidML-CPUvsGPU

PyQT5 based GUI application for performance comparison between Tensorflow (CPU) and PlaidML (GPU)

demoUI

Environment

CPU : Intel i7-8086K
GPU : Gigabyte Radeon RX Vega 64 Silver HBM2 8GB

OS : Ubuntu 18.04
Driver : AMD-GPUPRO 18.20

Python 3.5
Keras 2.2.2
Tensorflow 1.10
PlaidML 0.3.5

Usage

We used SSD (single shot multibox detector) model for internal object detection algorithm.
Also, we were able to develop this application with a lot of inspiration from the repository below.

Download the pre-trained weights from ssd_kerasV2 named VGG16SSD300weights_voc_2007_class20.hdf5
Save the weight file in the weight directory.

Create anaconda environment using environment.yml and following command:

$ conda env create

Activate virtual envitonment and start the application

$ conda activate demo
(demo) $ python main.py

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PyQT5 based GUI application for performance comparison between Tensorflow (CPU) and PlaidML (GPU)

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