Data augmentation for time series classification

Traffic Sign Detection CNN app

Script to randomly apply Geometric Transformations/Noise on an image, generating an output slightly different (or not). This is usually used in Machine Learning for data augmentation.

MINI PROJECTS AND VARIOUS CASE STUDY FOR CNN

This repository is a collection of PyTorch code examples, covering beginner to advanced topics, and including implementation of CNN models from scratch.

Explore CIFAR10 dataset through creating a Feed Forward Neural Network, training a CNN from scratch, and implementing advanced techniques like data normalization, augmentation, and ResNets in PyTorch to achieve over 90% accuracy.

Classifies whether an image is of a dog or cat using pre-trained models

A deep learning model for detecting and classifying various car parts, designed to assist firms in automating and optimizing parts identification and inventory management.

This is a data augmentation tool for creating augmented data from a bunch of images under directory with GUI.

Working on a computer vision project that can predict 9 different classes of tomato plant disease as well as a healthy plant.

Deep Learning: Traffic Sign Classifier with TensorFlow

Transformations for image + bboxes using PIL and Pytorch

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.

3 deep learning competitions consisting of Image Classification, Image Segmentation, Visual Question Answering

In this project, I have used Nvidia's CNN model to mimic human driver's behavior using a simulator.

Official release of the DMControl Generalization Benchmark 2 (DMC-GB2)

[KDD23] Official PyTorch implementation for "Improving Conversational Recommendation Systems via Counterfactual Data Simulation".

A light weight CLI for augmenting image datasets for deep learning and ML projects

Classification of flowers , by finetuning Resnets and Inception models also image augmentation and random image erasing