HistoClean is a tool for the preprocessing and augmentation of images used in deep learning models. This easy to use application brings together the most popular image processing packages from across the python universe, meaning no more looking at documentation! HistoClean provides real time feedback to augmentations and preprocessing options. T…

This script is used to augment image data created using LabelMe-MIT.

Facial Expression Classification Using Convolutional Neural Network And Residual Neural Networks.

Machine Learning based feature extraction of electrical substations from satellite data. Powered by IEEE-ICETCI, RRSC-Central, NRSC, and ISRO, this project incorporates instance segmentation of substations using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks.

Face recognition pipeline based on Facenet and MTCNN including image preprocessing (denoise, dehazing,...) with image augmentation techniques

kaleidoscope : Image augmentation in parallel

Object Detection and Bounding Box Prediction using YOLO5 and EfficientDet , Image Augmentations and Test Time Augmentations

This repository contains a notebook for Image Classification of Rock-Paper-Scissors's animated images.

Python Implementation of the paper : Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations.

Multiclass image classification of Bark-50 texture data from https://www.kaggle.com/datasets/saurabhshahane/barkvn50

It is a simple project where vehicle classification in malaysia for research purpose. This project will be use in deep learning apporach include efficientnet and inception

Face Detection from scratch

Exploring machine learning through diverse computer vision projects. Specializing in neural networks, CNNs, and algorithms. Projects include Human Detection, Face Recognition, and more. Experienced in data augmentation.

Files and Notebooks for Kaggle MNIST

(ImageDataGenerator) is Keras deep learning library provides the ability to use data augmentation automatically when training a model.

Computer aided Skin Tumor Detection for analysis and prediction of malignant skin lesions of the human body. The modelling is performed on the HAM10000 dataset and the dermatoscopic images of common pigmented skin lesions contained in the ISIC-SIIM archive.

Images should be preprocessed and prepared while preparing dataset. This repository contains a Colaboratory file which has a few codes used for image preprocessing for Machine learning.