Implementation of Exciting Computer Vision Projects

Mastering the Computer Vision : Working on Research dataset and Real world Projects.

References: Public Datasets, Competitions & Research Paper Implementation and Projects

Languages : Python, C++, Julia ...

DL Framework: Tensorflow, Keras, Pytorch, fastai, OpenCV, MxNet, Caffe, chainer...etc..

ML library : scikit-learn, numpy, pandas, matplot, seaborn, scipy, etc...

Special libraries : dask, numba, cupy, dlib, h5py, Pillow, PySpark, Tesseract

Day 1 : Face Recognition

Day 2 : OCR Simple

Day 3 : Keras - Imagenet Models

Day 4 : Keras - Transfer Learning and Fine Tuning for Image Classification

Day 5 : OpenCV - blob Detector, Center of Blob,

Day 6 : OpenCV - Exposure Fusion and HDR Imaging

Day 7 : Face Average, Facial landmark Detection and Attractiveness using OpenCV

Day 8 : Image Alingment and Metrics

Day 9 : Rotation matrix to Euler ANgles , heatmaps,

Day 10 : Eye Detector OpenCV

Day 11 : Non Photorealistic Rendering

Day 12 : OpenVINO using OpenCV @ Intel IOT,Edge Computing Computer Vision Woskshop @ INTEL Campus

Day 13 : Threshold Image Processing

Day 14 : Wrap Triangle , Delaunty

Day 15 : Object Tracking using OpenCV3..

Day 16 : Augument Reality Course using ARCore and Unity from Google VR& AR Coursera

Day 17 : Emerging Technologies in AR and Video Streaming from YONSEI University , Coursera

Day 18 : Denoising AutoEncoders

Day 19 : SSD for Object Detections

Day 20 : YOlo v2 and V3

Day 21 : MobileNet

Day 22 : ShuffleNet

Day 23 : DCGAN

Day 24 : Text to Image using Gan

Day 25 : Gans for Fashion

Day 26 : Face Recogntion and Landmark Alignment

Day 27 : Face 3D reconstruction

Day 28 : Neural Style Transfer

Day 29 : GAN for Image Colourization

Day 30 : Image to Image Translation Day 31 : Synthetic Data generation Using GAN