This repository contains demonstrations done with deep learning computer vision models.

fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.

Code release for "A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation", accepted by CVPR2023.

Hand-on experience in image classification by deep nerual network

Accurate image classification powered by InceptionV3 deep learning model. Quickly classify diverse images with high precision using TensorFlow.

Image Classification with Convolutional Neural Networks in TensorFlow: Fashion Item Classifier

A lightweight and extensible toolbox for image classification

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

CenOTAPH: COlour and Texture Analysis toolbox for PytHon

Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP

Image classification using deep learning models with activation map visualisation and TensorRT support

Deep learning basics

Implementations of various classifiers on MNIST dataset. Both traditional machine learning and deep learning methods are included.

[CVPR 2019] SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

Project code for Udacity's AI Programming with Python Nanodegree program. In this project, code was developed for an image classifier built with PyTorch, then converted into command line applications: train.py, predict.py.The image classifier recognizes different species of flowers.

Federated multicloud kubernetes cluster deployment using Infrastructure as Code for Image Classification

Web Based Image Recognition System in Python Flask

boostcamp AI Tech: 이미지 분류 대회

It is an image classification problem involving the usage of various custom and pretrained keras models on land classification dataset containing 21 classes.

Some of my Machine Learning projects