This repository bundles the implementation of some well-known Deep Neural Networks Architectures made for Computer Vision Task using Pytorch (why pytorch ? cuz Tensorflow is for boomers ! duh...).
In each folder, you will find the implmentation of the corresponding architecture, and a set of resources (articles, papers, videos...) that helped to understand the discussed architecture. If you want to understand the general folder structure, then head to /Deep Neural Networks, where you will find a generic DNN implementation along with descriptions for each folder and file.
I'll be working more on this repository in the upcoming time, as I have some architectures (and improvements) in mind to do. I'll try to summarize them here:
- Deep Neural Network
- Genrative Adversarial Networks (GANs)
- Deep Convolutional Genrative Adversarial Networks (DCGAN)
- Conditional Genrative Adversarial Networks (cGAN)
- Cycle Genrative Adversarial Networks (CycleGAN)
- Bicycle Genrative Adversarial Networks (BicycleGAN)
- Residual Networks
- GoogleLeNet (Inception)
- Inception v1
- Inception v2
- Inception v3
- Inception v4
- Inception-ReNet
- YOLO (You Only Look Once)
- YOLO V1
- YOLO V2
- YOLO V3
A huge thanks for Rohan Paul, and his Youtube series (which you find its code base here) for being a motivator of this repository. He had also helped a lot with understanding the original research papers and implementing those architectures.