Files and Notebooks for Kaggle MNIST
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Updated
Feb 1, 2019 - Jupyter Notebook
Files and Notebooks for Kaggle MNIST
kaleidoscope : Image augmentation in parallel
Object Detection and Bounding Box Prediction using YOLO5 and EfficientDet , Image Augmentations and Test Time Augmentations
Facial Expression Classification Using Convolutional Neural Network And Residual Neural Networks.
(ImageDataGenerator) is Keras deep learning library provides the ability to use data augmentation automatically when training a model.
This repository contains a notebook for Image Classification of Rock-Paper-Scissors's animated images.
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.
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.
This script is used to augment image data created using LabelMe-MIT.
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…
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.
Multiclass image classification of Bark-50 texture data from https://www.kaggle.com/datasets/saurabhshahane/barkvn50
This repository contains Python code for a project that performs American Sign Language (ASL) detection using multiclass classification. It utilizes YOLO (You Only Look Once) and MobileNetSSD_deploy for object detection, achieving an accuracy of 91%. The code offers options to predict signs from both images and videos.
Face Detection from scratch
Face recognition pipeline based on Facenet and MTCNN including image preprocessing (denoise, dehazing,...) with image augmentation techniques
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
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