Object_Localisation_Plus_Classification-From_Scratch

Build a model that does localization and classification(from scratch) on the Oxford IIT Data set

Objective of this notebook

• The purpose of this notebook is to build a model that performs object classification and localisation in one network
• Details of the problem statement , data set ,model architecture, summary of the code/solution , sample output/Prediction from the program and final result of the project are listed in the sections to follow.

Problem Statement

Build a model to that performs object classification and localisation

Data Description:

• Data Set :Oxford IIT Pet Data
• Link : https://www.robots.ox.ac.uk/~vgg/data/pets/

Model Architecture

Summary of the Solution/Code:

The code aims at building a simple bounding box to detect the animal's face

• We begin by building a utility class to to read the input images ,convert them to a structured format for further processing and model building and pickle the created input data files Code @ Read_& Structure_Input.ipynb
• We then do an EDA on the data and check various statistics on the images such as total no of images , whether classes are balanced and display a few images with their bounding boxes for visulaization . Code @ EDA.ipynb
• We will then we will pre-process the input data to make it compatible for model building wherein we first do a Train-Test split and create 2 subsets (70:30 split), convert all images to tensors for both subsets,resize all the images to one pre-defined size, convert bounding boxes accordingly,convert classification labels to one hot vectors ,visualize data and see that everything looks good. Code @ Pre-Processing_Data.ipynb
• We then build the model which is a keras image classifier with a base network of mobile net(pre-trained on image Net) and add a regression head and a classification head to it
• Finally we train the model , log the results and run an inference. Code @ Train_And_Inference.ipynb

Sample Ouput/Prediction :

Here is a sample Ouput image from the program/model

Result

• We built a model capable of doing object localisition + object classification in a single network
• The scores of the model as displayed in the logs were accuracy= 0.9955 and IoU = 0.9452 on the validation/testing data set

References & Guidance

• PGP GL/UAT study material
• https://learnopencv.com/classification-with-localization/