Rashmil-1999 / ASL_Detection

This project is created to provide quick image dataset preprocessing and then training a model and saving it. Detailed README coming

Quick development of Image Recognition model

(Used this for American Sign Language Detection-ASL using CNN's)

• Initial usage of this project was used for the detection of the various signs in the ASL and to help our end goal of integrating this in a mobile app to help the mute people better communicate with people who aren't cognizant of the language.

ASL Dataset link that I used from Kaggle.

• This project structure additionally helps to accelerate the development of image recognition models in a quick manner.

• All you have to know is that the preprocessing code is present in the prepare_data.py file if you are not bothered about the colour of the training images and only want to focus on the training then don't change the code here

How to use?

• Use the train_model.py file to train your model. But first you should modify the path parameters as per the dataset you are using.

• Place your dataset in the dataset folder. Then modify the parameters section in the train_model.py

• The various parameters that you would want to set would be

num_classes = Number of classes you want to classify eg. 29 > no_of_epochs = Required number of epochs or iterations you want. eg. 10 > size = A list of the width and height of the image for eg. [200, 200] > batch_size = Here you define the number of images to be fed. eg. 32

• More adjustments can be done to the section of:-

Network Parameters > Optimization Parameters

Note: The Network Parameters are dependent on the model you have defined in the cnn_model.py file be careful in defining the new params.

cnn_model.py

• Here you will define the Network architecture that you want.

• Based on the network that you have defined remember to make changes to the network parameters in the train_model.py file.

detector.py

• This file is still under development as it is the file that will carry out the detection part (the final stage) of the whole project.

• It contains hardcoded list of labels and preprocessing which will soon be converted to reading from csv and making a separate funciton for preprocessing the image(frame) captured from the video camera.