This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

Implementation based on the paper Li et al., “A Convolutional Neural Network Cascade for Face Detection, ” 2015 CVPR

The implement of Independent Cascade Model, with different seeds selection algorithms.

MLSS 2016 material.

Python implementation of Cascade information reconciliation protocol for Quantum Key Distribution

A Cascade Transformer-based Model for 3D Dose Distribution Prediction in Head and Neck Cancer Radiotherapy

The purpose of this project is to demonstrate the advantages of combining multiple CNNs to a common cascade structure. In contrast to training a single CNN only, the resulting classifier can be faster and more accurate at once. It can be used e.g. for the purpose of face detection.

BB84 Quantum Key Distribution implemented using the SimulaQron Python API

A Python Program using OpenCV to detect the maximum numbers of people present in the frame and show visualised analyse of the data

Delete functionality for normalised models

How to make a Smile Detector using opencv

Trained HAAR Cascade model for FRC 2020-2021 Infinite Recharge

Dynamics in signed social networks

Computer Vision and Implementations with Python

Cascade-Correlation Neural Network.

SiamBC Tracker

An image recognition process contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out with extreme simplicity, taking advantage of the ease of sklearn to implement the SVM model. Cascading face recognition is also used to refine the images, obtaining accuracy greater than 70% in the test with images that do not appe…