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