Research Paper Implementation

This project is written by Derek Thomas More interesting technical documentation is held on the github pages

-- Project Status: Active

Project Intro/Objective

The purpose of this project is to implement research papers. There are a few benefits I hope to achieve:

• Better research literacy
• Better Pytorch/Keras/Tensorflow skills
• Quicker idea to production pipelines
• Independant verification of results

Note: This was inspired by this post by kthx0

Methods Used

• Computer Vision
• Machine Learning
• Data Science

Technologies

• Python 3.6
  • PyTorch
  • Possibly Tensorflow/Keras in the future

Project Description

Architectures

AlexNet: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks

ZFNet: https://arxiv.org/abs/1311.2901

VGG16: https://arxiv.org/abs/1505.06798

ResNet: https://arxiv.org/abs/1704.06904

GoogLeNet: https://arxiv.org/abs/1409.4842

Inception: https://arxiv.org/abs/1512.00567

Xception: https://arxiv.org/abs/1610.02357

MobileNet: https://arxiv.org/abs/1704.04861

Semantic Segmentation

FCN: https://arxiv.org/abs/1411.4038

SegNet: https://arxiv.org/abs/1511.00561

UNet: https://arxiv.org/abs/1505.04597

PSPNet: https://arxiv.org/abs/1612.01105

DeepLab: https://arxiv.org/abs/1606.00915

ICNet: https://arxiv.org/abs/1704.08545

ENet: https://arxiv.org/abs/1606.02147

Generative adversarial networks

GAN: https://arxiv.org/abs/1406.2661

DCGAN: https://arxiv.org/abs/1511.06434

WGAN: https://arxiv.org/abs/1701.07875

Pix2Pix: https://arxiv.org/abs/1611.07004

CycleGAN: https://arxiv.org/abs/1703.10593

Object detection

RCNN: https://arxiv.org/abs/1311.2524

Fast-RCNN: https://arxiv.org/abs/1504.08083

Faster-RCNN: https://arxiv.org/abs/1506.01497

SSD: https://arxiv.org/abs/1512.02325

YOLO: https://arxiv.org/abs/1506.02640

YOLO9000: https://arxiv.org/abs/1612.08242

Getting Started

1. Clone this repo (for help see this tutorial).
2. Raw Data is being kept here within this repo.
3. Using the environment of your choice (I use Conda) install requirements.txt

Contact

• Feel free to contact me on my github page

Special Thanks for this Readme Template