architsharma97/ImageToolbox architsharma97/ImageToolbox

  • Stars
    star
    1
  • Language
    Python
  • Created about 7 years ago
  • Updated about 7 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
architsharma97/ImageToolbox
github

architsharma97

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

A simple toolbox which does Red-Eye Reduction and Segmentation based Selective Blurring of Images. Has a simple Tkinter based GUI, which accepts images and performs the selected functions.

More Repositories

1

dpo-rlaif

Jupyter Notebook
87
star
2

earl_benchmark

EARL: Environment for Autonomous Reinforcement Learning
Python
33
star
3

VideoSummarization

Course Project for CS771: Machine Learning
Python
25
star
4

medal

Code to reproduce results for MEDAL in PyTorch. Also contains code for running SAC and FBRL.
Python
4
star
5

CV

Personal CV, based on Alessandro Plasmati's.
TeX
2
star
6

VisualDialog

The repository contains Theano implementations for Visual Dialog Models (http://visualdialog.org/). In particular, the Late Fusion and Memory Network models have been implemented.
Python
2
star
7

VisualQA

A model for Visual Question Answering. Extracts image features using the vgg16 architecture. Extracts language features using LSTM. Outputs from both are combined using a fully connected MLP, outputs of which belong to pre-chosen set of most probable answers.
Python
2
star
8

mbsvme

Mixture of Bayesian SVM Experts
Python
1
star
9

architsharma97.github.io

Personal Website based on "Goa" for Hugo
HTML
1
star
10

InstanceRetrieval

Assignment for CS698O: Visual Recognition
Python
1
star
11

MNIST

Tests convergence of various estimators. In particular, PD and SF/REINFORCE are tested for gaussian and bernoulli latent variables and Synthetic gradients are tested in MNIST Classification (DNI).
Python
1
star
12

Privacy_SPMD

Privacy analsyis in DSRC: Data taken from Ann Arbor, Michigan. Conclusively shows that switching IDs randomly will not preserve user privacy is BSMs are transmitted at 10 Hz. Also shows lack of privacy if IDs are changed universally. Other protocols under test, where IDs are only transmitted when driving through an intersection.
Python
1
star