sub1120 / PSR-KD

Automated Shorthand Recognition using Optimized DNNs

Automated Shorthand Recognition using Optimized DNNs

This repository contains the supplementary files for our research article submitted for publication. Please contact us before using any material in this repository.

Authors: Subham Sahu, Dr. Francis Jesmar P. Montalbo, Yogesh Kumar Vishwakarma, Jeevanlal Kori.

Requirements

• Python 3.7.7
• tensorflow == 2.7.0
• tf-keras-vis == 0.6.2
• matplotlib == 3.5.1
• opencv-python==4.5.5.62
• numpy == 1.21.5
• tqdm == 4.62.3
• scikit-learn == 1.0.2
• Pillow == 9.0.1

Use following command pip install -r requirements.txt

Directories

• test.py : Defined functions to test models.
• gui.py : Interface Design Code.
• models : All trained models. models drive link
• ds : Dataset folder. dataset drive link
• utils/ : Defined all Custom functions required for Evaluation of Approach.
  1. load_utils.py: Defined functions to load trained models.
  2. cam_utils.py: Defined functions to produce CAMs.
  3. ensemble.py: Defined functions to make ensemble model.
  4. eval_utils.py: All other required functions are defined here.
• notebooks/ : Contains all Training, Evaluation and Other Notebooks.
  1. proposed student/: Contains proposed model training[with and withour KD] Notebooks.
  2. teacher candidates/: Contains teacher training Notebooks.
  3. ROC_PR_AUC.ipynb: Generate roc-pr plots.

Usage

Below command will open a Interface for testing our models.

python gui.py

To run script with GPU, use below command

python gui.py -g

Two Use Cases of Interface are as follows

• To Evaluate models [Test Accuracy, Validation Accuracy, FLOPs Count, Parameters Count].
• To Generate CAMs [Grad CAM, GradCam++, ScoreCam, Faster ScoreCam, Cameras, GuidedBP]

Interface Guide

PSR-.Tool.Demo.Video.mov

Note1: CAMERAS will take a while to generate cams.

Note2: For NasNetLarge & NasNetMobile use legacy-cameras branch to produce cameras cams.