This repository contains working examples of Neural Network Libraries. Before running any of the examples in this repository, you must install the Python package for Neural Network Libraries. The Python install guide can be found here.
Before running an example, also run the following command inside the example directory, to install additional dependencies:
cd example_directory
pip install -r requirements.txt
- Our Docker workflow offers an easy installation and setup of running environments of our examples.
- See this page.
We have prepared interactive demos, where you can play around without having to worry about the codes and the internal mechanism. You can run it directly on Colab from the links in the table below.
| Name | Notebook | Task |
|---|---|---|
| ESR-GAN |  |
Super-Resolution |
| Self-Attention GAN | |
Image Generation |
| Face Alignment Network | |
Facial Keypoint Detection |
| PSMNet | |
Depth Estimation |
| ResNet/ResNeXt/SENet | |
Image Classification |
| YOLO v2 | |
Object Detection |
| CenterNet | |
Object Detection |
| StarGAN | |
Image Translation |
| MixUp / CutMix / VH-Mixup | |
Data Augmentation |
| StyleGAN2 | |
Image Generation |
| X-UMX | |
Music Source Separation |
| DCGAN | |
Image Generation |
| Virtual Adversarial Training | |
Semi-Supervised Learning |
| Variational Auto-encoder | |
Unsupervised Learning |
| SiameseNet | |
Feature Embedding |
| Out-of-Core training | |
Out-of-Core training |