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.

SimplePY is a collection of Python scripts and utilities for file manipulation tailored for AI and ML projects. This repository aims to simplify the handling of datasets, model outputs, and other files commonly encountered in AI and machine learning workflows.

Classification model for Pizza_Steak dataset using CNN model.

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.

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

Files and Notebooks for Kaggle MNIST

kaleidoscope : Image augmentation in parallel

(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.

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

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

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.

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