ritwikraha Goto Github PK

a-guide-to-ml-workflows-with-jax

A Talk on ML Workflows with JAX for TFUG Kolkata

aero-app

Prototype of a flight app built using Adobe XD

asl-recognition-with-deep-learning

American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals. The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures. In this project, you will train a convolutional neural network to classify images of ASL letters. After loading, examining, and preprocessing the data, you will train the network and test its performance.

autoregressive-bhasha

A Collection of Language Models and Fine Tunings for Explorations

boardmerge

Command Line Python utility to Merge PDFs

cerebro

An interactive web application built with streamlit to visualise EEG data

computer-needs-glasses

place to practice my DL-CV skills

computer-vision-lung-cancer-detection

REU research detecting lung cancer from CT scans

cooperative-control

Masters project, designing a multi-vehicle control system.

coursera-dip

A public repository for all the Matlab codes for Coursera's Fundamentals of Digital Image Processing by Northwestern University

darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

differentiablebinarization

DB (Real-time Scene Text Detection with Differentiable Binarization) implementation in Keras and Tensorflow

diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

diffusion

Diffusion Models, Utilities and Pipelines in Torch TF and JAX

docs

TensorFlow documentation

document-scanner

A document scanner built using Open-CV python

ds_class

all the code in my data structure class

dsp

A collection of digital signal processing projects.

dsp-lab

A repository for dsp-lab matlab practice

edge-detector

An edge detection process using morphological operator in python

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

enigma

An Enigma cryptography project realized using JAVA.

esphomelib

esphomelib is a framework for using your ESP8266/ESP32 devices with Home Assistant

face-detection-in-python-using-opencv

Face Detection with Python using OpenCV

face-swapper

A repository for all the helper files for swapping faces using dlib python and opencv

feature-extraction

Computer vision feature extraction toolbox for image classification

fonts

:abc: A collection of 100% actually free open source fonts!

heal

An app that aims to heal the user through ambient music and appeasing imagery.

https-github.com-meagmohit-eeg-datasets