Implemented a Deep Learning Model to identify the fabric and classify it into different classes. Implemeted it using a Encoder with a CNN classifier, Siamese network for matching. The classification accuracy was 97 % on the test data. Can be implemented in major online fabric stores to facilitate the process of picking a fabric without looking at it. A project under Prof. Aditya Nigam of IIT Mandi.
Detecting from a video whether the video was taken in first person (Egocentric) or third person (Non-Egocentric). Using FlowNet2 for optical flow generation. And then using a ResNet50 model for classification, the classification was achieved. A project under Prof. Aditya Nigam of IIT Mandi.
A Deep Learning model to predict blood cancer in the microscopic image of slides. A UNet is also used for masking of the malignant cell for identification. Shipped with a light GUI built using Flask.
Trained on r/Science comments, the model is a LSTM based binary classifier. It understands the sentence semantics and gives a score to the sentence based on which either the comment is approved, or chanhes are suggested.
A network used to perform fourier transformations in GPU. Can be fine tuned for other applications too. The network uses a Convolutional Autoencoder with merge connections. Using residual learning, the model is easily able to learn the transformations of Fourier Transform and Inverse Fourier Transform. The Keras implementation is open sourced.
Created a Python Package for General Relativity. Funded by European Space Agency. NUMFOCUS Fiscal Sponsorship.
EinsteinPy is an open source pure Python package dedicated to problems arising in General Relativity and relativistic physics, such as geodesics calculation for vacuum solutions for Einstein's field equations.
An easy platform to get the sky coordinate of any celestial body. You have to know it's name ot RA, Dec. The project is under serious development.
Based on pyephem by Brandon Rhodes, it tries to analyse every body by it's ephemeris data. We have made a local database of various prominent stars in the nights sky as well as some other important objects.
A machine learning approach to detecting exoplanets of stars on the basis of kepler data. Use of FFT, DTW and Logistic Regression for performing the classification.