Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Overview

RealBasicVSR

[Paper]

This is the official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv". This repository contains codes, colab, video demos of our work.

Authors: Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Nanyang Technological University

Acknowedgement: Our work is built upon MMEditing. The code will also appear in MMEditing soon. Please follow and star this repository and MMEditing!

News

  • 29 Nov 2021: Test code released
  • 25 Nov 2021: Initialize with video demos

Table of Content

  1. Video Demos
  2. Code
  3. VideoLQ Dataset
  4. Citations

Video Demos

The videos have been compressed. Therefore, the results are inferior to that of the actual outputs.

output.mp4
output.mp4
output.mp4
output.mp4

Code

Installation

  1. Install PyTorch and torchvision following the official instructions, e.g.,
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
  1. Install mim and mmcv-full
pip install openmim
mim install mmcv-full
  1. Install mmedit
pip install mmedit

Inference

  1. Download the pre-trained weights to checkpoints/. (Dropbox / Google Drive)

  2. Run the following command:

python inference_realbasicvsr.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} --max-seq-len=${MAX_SEQ_LEN} --is_save_as_png=${IS_SAVE_AS_PNG}  --fps=${FPS}

This script supports both images and videos as inputs and outputs. You can simply change ${INPUT_DIR} and ${OUTPUT_DIR} to the paths corresponding to the video files, if you want to use videos as inputs and outputs. But note that saving to videos may induce additional compression, which reduces output quality.

For example:

  1. Images as inputs and outputs
python inference_realbasicvsr.py configs/realbasicvsr_x4.py checkpoints/RealBasicVSR_x4.pth data/demo_000 results/demo_000
  1. Video as input and output
python inference_realbasicvsr.py configs/realbasicvsr_x4.py checkpoints/RealBasicVSR_x4.pth data/demo_001.mp4 results/demo_001.mp4 --fps=12.5

Training

To be appeared.

VideoLQ Dataset

You can download the dataset using Dropbox or Google Drive.

Citations

@article{chan2021investigating,
  author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
  title = {Investigating Tradeoffs in Real-World Video Super-Resolution},
  journal = {arXiv preprint arXiv:2111.12704},
  year = {2021}
}
Owner
Kelvin C.K. Chan
Kelvin C.K. Chan
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

SelfReg PyTorch official implementation of Self-supervised Contrastive Regularization for Domain Generalization (SelfReg, https://arxiv.org/abs/2104.0

64 Dec 16, 2022
ICNet and PSPNet-50 in Tensorflow for real-time semantic segmentation

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

Oles Andrienko 219 Nov 21, 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training The Unreasonable Effectiveness of

VITA 44 Dec 23, 2022
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

safe-control-gym Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-ba

Dynamic Systems Lab 300 Dec 28, 2022
Boostcamp CV Serving For Python

Boostcamp-CV-Serving Prerequisites MySQL GCP Cloud Storage GCP key file Sentry Streamlit Cloud Secrets: .streamlit/secrets.toml #DO NOT SHARE THIS I

Jungwon Seo 19 Feb 22, 2022
This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.

Deformable Neural Radiance Fields This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. Project Page Paper Video This codebase conta

Google 1k Jan 09, 2023
Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning Reference Abeßer, J. & Müller, M. Towards Audio Domain Adapt

Jakob Abeßer 2 Jul 06, 2022
Semi-supervised semantic segmentation needs strong, varied perturbations

Semi-supervised semantic segmentation using CutMix and Colour Augmentation Implementations of our papers: Semi-supervised semantic segmentation needs

146 Dec 20, 2022
An experimental technique for efficiently exploring neural architectures.

SMASH: One-Shot Model Architecture Search through HyperNetworks An experimental technique for efficiently exploring neural architectures. This reposit

Andy Brock 478 Aug 04, 2022
Parris, the automated infrastructure setup tool for machine learning algorithms.

README Parris, the automated infrastructure setup tool for machine learning algorithms. What Is This Tool? Parris is a tool for automating the trainin

Joseph Greene 319 Aug 02, 2022
👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

🦕 👨‍💻 nanosaur_gazebo nanosaur The smallest NVIDIA Jetson dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS. Designed & ma

nanosaur 9 Jul 19, 2022
Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

Data Efficient Stagewise Knowledge Distillation Table of Contents Data Efficient Stagewise Knowledge Distillation Table of Contents Requirements Image

IvLabs 112 Dec 02, 2022
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes

FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes This repository contains the source code accompanying the paper: FlexConv: C

Robert-Jan Bruintjes 96 Dec 12, 2022
Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On

UPMT Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On See main.py as an example: from model import PopM

7 Sep 01, 2022
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments

Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of

300 Nov 22, 2022
StarGAN2 for practice

StarGAN2 for practice This version of StarGAN2 (coined as 'Post-modern Style Transfer') is intended mostly for fellow artists, who rarely look at scie

vadim epstein 87 Sep 24, 2022
Fast convergence of detr with spatially modulated co-attention

Fast convergence of detr with spatially modulated co-attention Usage There are no extra compiled components in SMCA DETR and package dependencies are

peng gao 135 Dec 07, 2022
This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".

Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement This is the repository for the paper "Improving the Accuracy-Memory Trad

3 Dec 29, 2022