Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption

Last update: Jun 07, 2022

Related tags

Deep Learning SG-GAN

Overview

SG-GAN

TensorFlow implementation of SG-GAN.

Prerequisites

TensorFlow (implemented in v1.3)
numpy
scipy
pillow

Getting Started

Train

Prepare dataset. We present example images in datasets folder for reference as data format, and scripts prepare_data.py and segment_class.py for reference in preparing dataset.
Train a model:

CUDA_VISIBLE_DEVICES=0 python main.py

Models are saved to ./checkpoints/ (can be changed by passing --checkpoint_dir=your_dir).

Continue training a model (useful for updating parameters)

CUDA_VISIBLE_DEVICES=0 python main.py --continue_train 1

Test

Finally, test the model:

CUDA_VISIBLE_DEVICES=0 python main.py --phase test --img_width 2048 --img_height 1024

Adapted test images will be outputted to ./test/ (can be changed by passing --test_dir=your_dir).

Reference

The TensorFlow implementation of CycleGAN, https://github.com/xhujoy/CycleGAN-tensorflow

Owner

lplcor

Machine Learning Artist 👩‍🎨 (CV, DL, AI)

GitHub Repository https://arxiv.org/abs/1801.01726

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