LONDONSPICY

LONDONSPICY's Stars

• artem-konevskikh/stylegan2-ada-pytorch

  StyleGAN2-ADA - Official PyTorch implementation

  Language:Jupyter Notebook1

• rzimmerdev/pdi

  Implementation of a pixel-wise segmentation task for classification of different building and terrain types.

  Language:Python2

• reachsumit/deep-unet-for-satellite-image-segmentation

  Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet

  Language:Python309139

• ayushdabra/dubai-satellite-imagery-segmentation

  Multi-Class Semantic Segmentation on Dubai's Satellite Images.

  Language:Jupyter Notebook8028

• ayushdabra/drone-images-semantic-segmentation

  Multi-class semantic segmentation performed on "Semantic Drone Dataset."

  Language:Jupyter Notebook7218

• dvschultz/stylegan2-ada-pytorch

  StyleGAN2-ADA - Official PyTorch implementation

  Language:Jupyter Notebook245122

• fastai/fastai1

  v1 of the fastai library. v2 is the current version. v1 is still supported for bug fixes, but will not receive new features.

  Language:Jupyter Notebook101114

• krasserm/super-resolution

  Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution

  Language:Python1.5k353

• dariopavllo/textured-3d-gan

  Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

  Language:Python15525

• mohammedbehjoo/-artificial-intelligence-in-architecture-exploring-GANs

  This workshop is going to be seven sessions of 3 hours. It consists of three major parts: first, Fundamentals of deep learning, which is about the history of artificial intelligence and the relationship between AI, machine learning, and deep learning. It is also about the mathematics of neural networks. After that, we are going to learn about the basics of neural networks and machine learning. Second, we are going more in-depth in computer vision; we train a neural network from scratch. Then we learn about “transfer learning,” which means how to use a pre-trained neural network. Finally, and most importantly, the last part is about “Generative adversarial networks” (GAN). We will have some experiments with “ neural style transfer” as an example. After that, we will learn about GANs, specifically CycleGAN, and how to implement them. After this workshop, you know the basics of artificial intelligence, the relationship between machine learning and deep learning. You understand how neural networks work; you can make and implement deep learning models to classify images. You can also make Generative Adversarial Networks to synthesize new images and broaden your horizons.

  Language:Jupyter Notebook5

• mohammedbehjoo/DigitalFUTURES-Developing-Costum-Components-in-Grasshopper-with-Python

  This is a repository for a Tutorial for DigitalFUTURES World.

  Language:Python52

• garder14/cyclegan-tensorflow2

  A simple implementation of CycleGAN for unsupervised map-to-aerial and aerial-to-map translation (TensorFlow 2)

  Language:Python1

• balciozan/cycleGAN_GameMapGenerator

  CycleGAN Game Map Generator

  Language:Jupyter Notebook11

• geraseva/cyclegan_maps

  Using cycleGAN to transform satellite views into DnD maps

  Language:Jupyter Notebook1