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

Related tags

Deep LearningSG-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

Owner
lplcor
Machine Learning Artist 👩‍🎨 (CV, DL, AI)
lplcor
Grounding Representation Similarity with Statistical Testing

Grounding Representation Similarity with Statistical Testing This repo contains code to replicate the results in our paper, which evaluates representa

26 Dec 02, 2022
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs Abstract: Image-to-image translation has recently achieved re

yaxingwang 23 Apr 14, 2022
Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation by Lukas Hoyer, Dengxin Dai, and Luc Van Gool [Arxiv] [Paper] Overview Unsup

Lukas Hoyer 149 Dec 28, 2022
A Python Package For System Identification Using NARMAX Models

SysIdentPy is a Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license. N

Wilson Rocha 175 Dec 25, 2022
Tooling for the Common Objects In 3D dataset.

CO3D: Common Objects In 3D This repository contains a set of tools for working with the Common Objects in 3D (CO3D) dataset. Download the dataset The

Facebook Research 724 Jan 06, 2023
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

Despoina Paschalidou 161 Dec 20, 2022
AdaFocus (ICCV 2021) Adaptive Focus for Efficient Video Recognition

AdaFocus (ICCV 2021) This repo contains the official code and pre-trained models for AdaFocus. Adaptive Focus for Efficient Video Recognition Referenc

Rainforest Wang 115 Dec 21, 2022
HW3 ― GAN, ACGAN and UDA

HW3 ― GAN, ACGAN and UDA In this assignment, you are given datasets of human face and digit images. You will need to implement the models of both GAN

grassking100 1 Dec 13, 2021
[CoRL 21'] TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo

TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo Lukas Koestler1*    Nan Yang1,2*,†    Niclas Zeller2,3    Daniel Cremers1

TUM Computer Vision Group 744 Jan 04, 2023
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
Project page for our ICCV 2021 paper "The Way to my Heart is through Contrastive Learning"

The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video This is the official project page of our ICCV 2

36 Jan 06, 2023
CRNN With PyTorch

CRNN-PyTorch Implementation of https://arxiv.org/abs/1507.05717

Vadim 4 Sep 01, 2022
Prototype python implementation of the ome-ngff table spec

Prototype python implementation of the ome-ngff table spec

Kevin Yamauchi 8 Nov 20, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
pytorch implementation of dftd2 & dftd3

torch-dftd pytorch implementation of dftd2 [1] & dftd3 [2, 3] Install # Install from pypi pip install torch-dftd # Install from source (for developer

33 Nov 28, 2022
Pointer-generator - Code for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks

Note: this code is no longer actively maintained. However, feel free to use the Issues section to discuss the code with other users. Some users have u

Abi See 2.1k Jan 04, 2023
Teaches a student network from the knowledge obtained via training of a larger teacher network

Distilling-the-knowledge-in-neural-network Teaches a student network from the knowledge obtained via training of a larger teacher network This is an i

Abhishek Sinha 146 Dec 11, 2022
Bulk2Space is a spatial deconvolution method based on deep learning frameworks

Bulk2Space Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space Bulk2Space is a spatial deconvolution method based on

Dr. FAN, Xiaohui 60 Dec 27, 2022
DvD-TD3: Diversity via Determinants for TD3 version

DvD-TD3: Diversity via Determinants for TD3 version The implementation of paper Effective Diversity in Population Based Reinforcement Learning. Instal

3 Feb 11, 2022
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022