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adding-waterdroplets-to-image

Generating water droplets in real-time images using Computer Vision.

age-gender-prediction

Using deep-learning pre-trained model, trying to predict the age and gender with the better performance

argoverse2.0-data

This is for argoverse data understanding. Motion forecasting data is being explored.

augmentor

Image augmentation library in Python for machine learning.

awesome-deep-learning

This repositories includes content of Deep Learning which includes books, courses, videos, lectures and more. This could be useful for Deep learning beginners as well as intermediate-experienced in Deep learning.

caffemodel_dataprocessing

How to convert images with different labes in train.txt and val.txt file for creating the caffemodel

carnd-advanced-lane-lines

carnd-vehicle-detection

This repository contains the python-OpenCV implementation of car/vehicle detection. Using basic feature extraction techniques in computer vision HOG, I re-implemented and retrained the model with not so promising but 85% accuracy.

ci-cd-githubactions

computer-visions-in-opencv

The most common concepts of computer vision has been implemented in python. The code/concepts has been followed from PyImageSearch tutorials.

cplusplus-practice

In this repository I have implemented C++ code for all basics level to intermediate level of c++concepts, also commented the concepts for better understanding.

data-augmentation

data-aumentation-of-images-folder-using-keras-and-pil

We're creating the data augmentation for further process of training and testing in model for better accuracy.

digit-recognition

docker-kubernetes

dvc_understanding

This is for understanding for DVC, A machine Learning tool which can track the data, Experiments and metrics.

emotion-detection-vggnet-architecture-fer2013

I have used haarcascade.xml and deep learning techniques to detect the emotion of person. Trained in keras framework using fer2013 dataset. I have delivered the model with almost 95.0% of accuracy. Credit: PyImageSearch

ensemble_method_dl

extracting-images-from-video

Sample code for extracting images from video. Used PIL Library and OpenCV for extracting image. The main goal of writing this code is "for very large video size file, it is difficult sometimes to extract in arranged way; so, This code will help to extract image from video in same named -new folder "

face-and-eye-detection_opencv

This minor project repositories includes the code for detecting the face and eye using haarcascade_frontalface_default.xml and haarcascade_eye.xml. It takes input as images and returns output as detected face and eye in bouding box.

face-landmarks-and-alignment

Step by step guide to install dlib in linux. Implemented in python which includes dlib, opencv library that detects the facial landmarks and alignment of faces in any images; the predictor trained on Helen data-set..

fog-image-creation

This project is about adding fog/Haze effects in the image.

git_dvc_test

Just to play around with git and dvc

google-drive-in-linux

Step by step guidelines for integrating google drive in linux. If you do not prefer the command line, Using Grive2 tool we can access. It is incredibly easy to use FUSE-based system written in Ocaml.

googlenet-architecture-in-deeplearning-using-imagenet

hands-detection

Hands video tracker using the Tensorflow Object Detection API and Faster RCNN model. The data used is the Hand Dataset from University of Oxford.

image-orientation-correction-using-cvpr-dataset

Using transfer learning techniques, I have trained the model that can check image orientation and If images are not in right orientation that images can be converted in right orientation in inference.

learnopencv

Learn OpenCV : C++ and Python Examples

machine-learning

This repository include python implementation of basic and intermediate level machine learning algorithms.

minivggnet-architecture-on-cifar10-dataset

This repository contains VGGnet architecture in keras for image classification. I trained miniVGGnet with 10 classes & achieved training accuracy up to 85% and testing accuracy up to 80%.