abhishekchopde

abhishekchopde.github.io

image_upload_app_django

Containerized django application using Dockers

invertible-resnet-1

Official Code for Invertible Residual Networks

LatexTemplate-AutoML-Conf

Latex Template for AutoML-Conf

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mnist-digit-classifier-torch

This repo contains code base for classifying 0-9 digits from MNIST dataset. I have implemented a CNN.

Quality-Inspection-of-Cocoons-using-Computer-Vision

This project aims at inspecting the quality and the quantity of silk inside cocoons using computer vision. It establishes the relationship between physical parameters (area,volume,perimeter) and quantity of silk inside a cocoon shell. It also aims at classifying the cocoons based on their texture quality. At the end of this project we concluded quality of silk inside a cocoon shell depends on the quality of the texture.

abhishekchopde/abhishekchopde.github.io

abhishekchopde/image_upload_app_django

Containerized django application using Dockers

abhishekchopde/invertible-resnet-1

Official Code for Invertible Residual Networks

abhishekchopde/maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

abhishekchopde/mnist-digit-classifier-torch

This repo contains code base for classifying 0-9 digits from MNIST dataset. I have implemented a CNN.

abhishekchopde/Quality-Inspection-of-Cocoons-using-Computer-Vision

This project aims at inspecting the quality and the quantity of silk inside cocoons using computer vision. It establishes the relationship between physical parameters (area,volume,perimeter) and quantity of silk inside a cocoon shell. It also aims at classifying the cocoons based on their texture quality. At the end of this project we concluded quality of silk inside a cocoon shell depends on the quality of the texture.