/CV-Project-Image-Transform-Prediction-Model-Analysis

This project acts as a pipeline to perform certain image transformations as a method to preprocess images, and feeding those transformed images though CNN models. Depending on the input classes, different features will be extracted, and a good predictive model can be determined for the usecase.

Primary LanguageJupyter Notebook

CV-Project

Image Transform Prediction Model Analysis

This project acts as a pipeline to perform certain image transformations as a method to preprocess images, and feeding those transformed images though CNN models. Depending on the input classes, different features will be extracted, and a good predictive model can be determined for the usecase.

Four transforms are used in this project :

Four pre-trained models (with learnable and un-learnable parameters) have been implemented as well :

This project has the following features :

  • The input can contain any number of classes. The models will adjust accordingly.
  • The model has been deployed on a web-based UI hosted on streamlit for ease of use.
  • We get a learning graph at the end as outputs for comparing the learning of the models form these four models.

Input Format

The input should be a .zip file containing number of folders the same as the number of classes in the dataset. Each of those folders need to contain images of those classes.

image image image

Output

The output is in the form of learning graph showing learning curve of the transformed images as the base data. canny_acc canny_loss

Original setup

  1. Windows 10 64-bit.
  2. Python 3.10.2
  3. Nvidia GTX 1050m

Project Setup

  1. Clone this repository using git clone https://github.com/accelbia/CV-Project-Wavelet-Transform-Prediction-Model-Analysis.
  2. Navigate to the current cloned repository.
  3. Install the necessary requirements using pip install -r requirements.txt.
  4. Run the streamlit application by running streamlit run Home.py.

Trial Run

  1. Upload the dataset by clicking on the 'Browse files'. image

  2. The classes contained in the zip files are displayed. Choose the model you want to use and add in the relevant layers and information. image

  3. Choose the optimizer, loss function and metrics, and click start to start the training. image

  4. The training information is displayed on the screen after the training. image image

The project was made by

image CB.EN.U4CSE19106 - Ayush Barik
CB.EN.U4CSE19106 - T. Ganesh Rohit Sarma
CB.EN.U4CSE19106 - A Shivaani
CB.EN.U4CSE19106 - V Neelesh Gupta