A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

Overview

PokeGAN

A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

Dataset

The model has been trained on dataset that includes 819 pokémon.
You can download dataset from this kaggle link.

Dependencies

I have used the following versions for code work:

  • python==3.8.8
  • tensorflow==2.4.1
  • tensorflow-gpu==2.4.1
  • numpy==1.19.1
  • h5py==2.10.0

Note

There are several difficulties in pokemon generation using GAN :

  • The difficulty of GAN training is well known; changing a hyperparameter can greatly change the results.
  • The dataset size is too small! 819 different pokemon images are not enough. For this reason, I applied data augmentation on the data; these are the transformations applied :
img_transf = tf.keras.Sequential([
            	tf.keras.layers.experimental.preprocessing.RandomContrast(factor=(0.05, 0.15)),
                image_aug.RandomBrightness(brightness_delta=(-0.15, 0.15)),
                image_aug.PowerLawTransform(gamma=(0.8,1.2)),
                image_aug.RandomSaturation(sat=(0, 2)),
                image_aug.RandomHue(hue=(0, 0.15)),
                tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
	    	tf.keras.layers.experimental.preprocessing.RandomTranslation(height_factor=(-0.10, 0.10), width_factor=(-0.10, 0.10)),
		tf.keras.layers.experimental.preprocessing.RandomZoom(height_factor=(-0.10, 0.10), width_factor=(-0.10, 0.10)),
		tf.keras.layers.experimental.preprocessing.RandomRotation(factor=(-0.10, 0.10))])
  • StyleGAN training is very expensive! I trained the model starting from a 4x4 resolution up to the final resolution of 256x256. The model was trained for 8 days using a Tesla V100 32GB SXM2.
    To get better results you need to use higher resolutions and train for longer time.

Results

These are some examples of new pokémon generated by the model :

New Generated Pokémon

More results

You can see hundreds of new pokemon here.
I repeat again it : to get better results (better details in pokemon) is necessary to train for more time.

References

This code implementation is inspired by the unofficial keras implementation of styleGAN.

Owner
I love computer vision. I love artificial intelligence. Machine Learning and Big Data master's degree student.
Styled Handwritten Text Generation with Transformers (ICCV 21)

⚡ Handwriting Transformers [PDF] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan & Mubarak Shah Abstract: We

Ankan Kumar Bhunia 85 Dec 22, 2022
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure

Phil Wang 19 May 06, 2022
Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Introduction This is an official release of the paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity" (arxiv link). Abstrac

Leo 21 Nov 23, 2022
Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

Lucas 103 Dec 14, 2022
SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab

CORNELLSASLAB SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab Instructions: This python code can be used to convert SAS out

2 Jan 26, 2022
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s

Yi-Hsuan Tsai 782 Dec 30, 2022
This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Microsoft 408 Dec 30, 2022
Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

41 Jan 03, 2023
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers

Benedek Rozemberczki 4.5k Jan 01, 2023
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise

45 Dec 08, 2022
Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.

NN Template Generic template to bootstrap your PyTorch project. Click on Use this Template and avoid writing boilerplate code for: PyTorch Lightning,

Luca Moschella 520 Dec 30, 2022
The codes of paper 'Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees'

Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees This project contains the codes of pap

0 Apr 20, 2022
Code for "Universal inference meets random projections: a scalable test for log-concavity"

How to use this repository This repository contains code to replicate the results of "Universal inference meets random projections: a scalable test fo

Robin Dunn 0 Nov 21, 2021
Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"

MMO: Meta Multi-Objectivization for Software Configuration Tuning This repository contains the data and code for the following paper that is currently

0 Nov 17, 2021
Codes for the AAAI'22 paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning"

TransZero [arXiv] This repository contains the testing code for the paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning" accepted to

Shiming Chen 52 Jan 01, 2023
OMAMO: orthology-based model organism selection

OMAMO: orthology-based model organism selection OMAMO is a tool that suggests the best model organism to study a biological process based on orthologo

Dessimoz Lab 5 Apr 22, 2022
Code for C2-Matching (CVPR2021). Paper: Robust Reference-based Super-Resolution via C2-Matching.

C2-Matching (CVPR2021) This repository contains the implementation of the following paper: Robust Reference-based Super-Resolution via C2-Matching Yum

Yuming Jiang 151 Dec 26, 2022
2.86% and 15.85% on CIFAR-10 and CIFAR-100

Shake-Shake regularization This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake

Xavier Gastaldi 294 Nov 22, 2022
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. The related paper is avai

26 Dec 13, 2022
Curated list of awesome GAN applications and demo

gans-awesome-applications Curated list of awesome GAN applications and demonstrations. Note: General GAN papers targeting simple image generation such

Minchul Shin 4.5k Jan 07, 2023