Contains notebooks for the PAR tutorial at CVPR 2021.

This repository provides a Colab Notebook that shows how to use Spatial Transformer Networks inside CNNs in Keras.

FULL Implementation of RCNN from scratch

Computer vision container that includes Jupyter notebooks with built-in code hinting, Anaconda, CUDA 11.8, TensorRT inference accelerator for Tensor cores, CuPy (GPU drop in replacement for Numpy), PyTorch, PyTorch geometric for Graph Neural Networks, TF2, Tensorboard, and OpenCV for accelerated workloads on NVIDIA Tensor cores and GPUs.

Example for how folder structure and config file for multiple model hosting looks like with OpenVino

Accompanying notebooks for media processing lecture at the University of Oldenburg

Digit recognition neural network

Detection of a text on a image using opencv and pytesseract

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an OBJECT DETECTION proxia when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an OBJECT MASKING PROXIA when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a TRAFFIC COUNTING (TC) PROXIA when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an ASTRAL VISION PROXIA (C) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a FACE RECOGNITION PROXIA (B) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY)

Contained in this repository are the Jupyter notebooks that contain the scripts used in this project. Examples include: exploratory data analysis, creation of training, validation and test data sets, and CNN model development and data extraction.

This repository contains all code for the website which is part of the Machine Vision course teached to the Masters of Engineering Technology at KU Leuven Bruges. It contains several notebooks that illustrate different aspects that were discussed in the course.