CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

Overview

CaFM-pytorch ICCV ACCEPT

Introduction of dataset VSD4K

Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city. Each category is consisted of various video length, including: 15s, 30s, 45s, etc. For a specific category and its specific video length, there are 3 scaling factors: x2, x3 and x4. In each file, there are HR images and its corresponding LR images. 1-n are training images , n - (n + n/10) are test images. (we select test image 1 out of 10). The dataset can be obtained from [https://pan.baidu.com/s/14pcsC7taB4VAa3jvyw1kog] (passward:u1qq) and google drive [https://drive.google.com/drive/folders/17fyX-bFc0IUp6LTIfTYU8R5_Ot79WKXC?usp=sharing].

e.g.:game 15s
dataroot_gt: VSD4K/game/game_15s_1/DIV2K_train_HR/00001.png
dataroot_lqx2: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X2/00001_x2.png
dataroot_lqx3: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X3/00001_x3.png
dataroot_lqx4: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X4/00001_x4.png

Proposed method

Introduction

Our paper "Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation" has been submitted to 2021 ICCV. we aim to use super resolution network to improve the quality of video delivery recently. The whole precedure is shown below. We devide the whole video into several chunks and apply a joint training framework with Content aware Feature Module(CaFM) to train each chunk simultaneously. With our method, each video chunk only requires less than 1% of original parameters to be streamed, achieving even better SR performance. We conduct extensive experiments across various SR backbones(espcn,srcnn,vdsr,edsr16,edsr32,rcan), video time length(15s-10min), and scaling factors(x2-x4) to demonstrate the advantages of our method. All pretrain models(15s, 30s, 45s) of game category can be found in this link [https://pan.baidu.com/s/1P18FULL7CIK1FAa2xW56AA] (passward:bjv1) and google drive link [https://drive.google.com/drive/folders/1_N64A75iwgbweDBk7dUUDX0SJffnK5-l?usp=sharing].

Figure 1. The whole procedure of adopting content-aware DNNs for video delivery. A video is first divided into several chunks and the server trains one model for each chunk. Then the server delivers LR video chunks and models to client. The client runs the inference to super-resolve the LR chunks and obtain the SR video.

Quantitative results

We show our quantitative results in the table below. For simplicity, we only demonstrate the results on game and vlog datasets. We compare our method M{1-n} with M0 and S{1-n}. The experiments are conducted on EDSR.

  • M0: a EDSR without CaFM module, train on whole video.
  • Si: a EDSR without a CaFM module, train on one specific chunk i.
  • M{1-n}ours: a EDSR with n CaFM modules, train on n chunks simultaneously.
Dataset Game15s Game30s Game45s
Scale x2 x3 x4 x2 x3 x4 x2 x3 x4
M0 42.24 35.88 33.44 41.84 35.54 33.05 42.11 35.75 33.33
S{1-n} 42.82 36.42 34.00 43.07 36.73 34.17 43.22 36.72 34.32
M{1-n} Ours 43.13 37.04 34.47 43.37 37.12 34.58 43.46 37.31 34.79
Dataset Vlog15s Vlog30s Vlog45s
Scale x2 x3 x4 x2 x3 x4 x2 x3 x4
M0 48.87 44.51 42.58 47.79 43.38 41.24 47.98 43.58 41.53
S{1-n} 49.10 44.80 42.83 48.20 43.68 41.55 48.48 44.12 42.12
M{1-n} Ours 49.30 45.03 43.11 48.55 44.15 42.16 48.61 44.24 42.39

Quatitative results

We show the quatitative results in the figure below.

  • bicubic: SR images are obtained by bicubic
  • H.264/H.265: use the default setting of FFmpeg to generate the H.264 and H.265 videos

Dependencies

  • Python >= 3.6
  • Torch >= 1.0.0
  • opencv-python
  • numpy
  • skimage
  • imageio
  • matplotlib

Quickstart

M0 demotes the model without Cafm module which is trained on the whole dataset. S{1-n} denotes n models that trained on n chunks of video. M{1-n} demotes one model along with n Cafm modules that trained on the whole dataset. M{1-n} is our proposed method.

How to set data_range

n is the total frames in a video. We select one test image out of 10 training images. Thus, in VSD4K, 1-n is its training dataset, n-(n+/10) is the test dataset. Generally, we set 5s as the length of one chunk. Hence, 15s consists 3 chunks, 30s consists 6 chunks, etc.

Video length(train images + test images) chunks M0/M{1-n} S1 S2 S3 S4 S5 S6 S7 S8 S9
15s(450+45) 3 1-450/451-495 1-150/451-465 151-300/466-480 301-450/481-495 - - - - - -
30s(900+95) 6 1-900/901-990 1-150/901-915 151-300/916-930 301-450/931-945 451-600/946-960 601-750/961-975 751-900/976-990 - - -
45s(1350+135) 9 1-1350/1351-1485 1-150/1351-1365 151-300/1366-1380 301-450/1381-1395 451-600/1396-1410 601-750/1411-1425 751-900/1426-1440 901-1050/1441-1455 1051-1200/1456-1470 1201-1350/1471-1485

Train

For simplicity, we only demonstrate how to train 'game_15s' by our method.

  • For M{1-n} model:
CUDA_VISIBLE_DEVICES=3 python main.py --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --scale {scale factor} --patch_size {patch size} --save {name of the trained model} --reset --data_train DIV2K --data_test DIV2K --data_range {train_range}/{test_range} --cafm --dir_data {path of data} --use_cafm --batch_size {batch size} --epoch {epoch} --decay {decay} --segnum {numbers of chunk} --length
e.g. 
CUDA_VISIBLE_DEVICES=3 python main.py --model EDSR --scale 2 --patch_size 48 --save trainm1_n --reset --data_train DIV2K --data_test DIV2K --data_range 1-450/451-495 --cafm --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --batch_size 64 --epoch 500 --decay 300 --segnum 3 --is15s

You can apply our method on your own images. Place your HR images under YOURS/DIV2K_train_HR/, with the name start from 00001.png. Place your corresponding LR images under YOURS/DIV2K_train_LR_bicubic/X2, with the name start from 00001_x2.png.

e.g.:
dataroot_gt: YOURS/DIV2K_train_HR/00001.png
dataroot_lqx2: YOURS/DIV2K_train_LR_bicubic/X2/00001_x2.png
dataroot_lqx3: YOURS/DIV2K_train_LR_bicubic/X3/00001_x3.png
dataroot_lqx4: YOURS/DIV2K_train_LR_bicubic/X4/00001_x4.png
  • The running command is like:
CUDA_VISIBLE_DEVICES=3 python main.py --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --scale {scale factor} --patch_size {patch size} --save {name of the trained model} --reset --data_train DIV2K --data_test DIV2K --data_range {train_range}/{test_range} --cafm --dir_data {path of data} --use_cafm --batch_size {batch size} --epoch {epoch} --decay {decay} --segnum {numbers of chunk} --length
  • For example:
e.g. 
CUDA_VISIBLE_DEVICES=3 python main.py --model EDSR --scale 2 --patch_size 48 --save trainm1_n --reset --data_train DIV2K --data_test DIV2K --data_range 1-450/451-495 --cafm --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --batch_size 64 --epoch 500 --decay 300 --segnum 3 --is15s

Test

For simplicity, we only demonstrate how to run 'game' category of 15s. All pretrain models(15s, 30s, 45s) of game category can be found in this link [https://pan.baidu.com/s/1P18FULL7CIK1FAa2xW56AA] (passward:bjv1) and google drive link [https://drive.google.com/drive/folders/1_N64A75iwgbweDBk7dUUDX0SJffnK5-l?usp=sharing].

  • For M{1-n} model:
CUDA_VISIBLE_DEVICES=3 python main.py --data_test DIV2K --scale {scale factor} --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --test_only --pre_train {path to pretrained model} --data_range {train_range} --{is15s/is30s/is45s} --cafm  --dir_data {path of data} --use_cafm --segnum 3
e.g.:
CUDA_VISIBLE_DEVICES=3 python main.py --data_test DIV2K --scale 4 --model EDSR --test_only --pre_train /home/CaFM-pytorch/experiment/edsr_x2_p48_game_15s_1_seg1-3_batch64_k1_g64/model/model_best.pt --data_range 1-150 --is15s --cafm  --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --segnum 3

Additional

We also demonstrate our method in vimeo dataset and HEVC test sequence. These datasets and all trained models will be released as soon as possible. By the way, we add SEFCNN.py into our backbone list which is suggested by reviewer.The code will be updated regularly.

Acknowledgment

AdaFM proposed a closely related method for continual modulation of restoration levels. While they aimed to handle arbitrary restoration levels between a start and an end level, our goal is to compress the models of different chunks for video delivery. The reader is encouraged to review their work for more details. Please also consider to cite AdaFM if you use the code. [https://github.com/hejingwenhejingwen/AdaFM]

BlueFog Tutorials

BlueFog Tutorials Welcome to the BlueFog tutorials! In this repository, we've put together a collection of awesome Jupyter notebooks. These notebooks

4 Oct 27, 2021
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

Denis 29 Nov 21, 2022
Automatically download the cwru data set, and then divide it into training data set and test data set

Automatically download the cwru data set, and then divide it into training data set and test data set.自动下载cwru数据集,然后分训练数据集和测试数据集

6 Jun 27, 2022
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022
Anomaly detection related books, papers, videos, and toolboxes

Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify

Yue Zhao 6.7k Dec 31, 2022
Implementation of the paper Scalable Intervention Target Estimation in Linear Models (NeurIPS 2021), and the code to generate simulation results.

Scalable Intervention Target Estimation in Linear Models Implementation of the paper Scalable Intervention Target Estimation in Linear Models (NeurIPS

0 Oct 25, 2021
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名:W03KFgNOc 排名:3 正确率: 0.75564 队员:yyMoming,xkwang,RichardoMu。 比赛链接:人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
Image Captioning on google cloud platform based on iot

Image-Captioning-on-google-cloud-platform-based-on-iot - Image Captioning on google cloud platform based on iot

Shweta_kumawat 1 Jan 20, 2022
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Nervana 3.9k Dec 20, 2022
A large-scale database for graph representation learning

A large-scale database for graph representation learning

Scott Freitas 29 Nov 25, 2022
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022
Official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

GLIDE This is the official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing w

OpenAI 2.9k Jan 04, 2023
Hand-distance-measurement-game - Hand Distance Measurement Game

Hand Distance Measurement Game This is program is made to calculate the distance

Priyansh 2 Jan 12, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
Distributed Asynchronous Hyperparameter Optimization in Python

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

6.5k Jan 01, 2023
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022
Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

YOLaT-VectorGraphicsRecognition This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without

Microsoft 49 Dec 20, 2022
Mining-the-Social-Web-3rd-Edition - The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)

Mining the Social Web, 3rd Edition The official code repository for Mining the Social Web, 3rd Edition (O'Reilly, 2019). The book is available from Am

Mikhail Klassen 838 Jan 01, 2023
Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Introduction 1. Usage (For MSS) 1.1 Prepare running environment 1.2 Use pretrained model 1.3 Train new MSS models from scratch 1.3.1 How to train 1.3.

Leo 100 Dec 25, 2022
MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

Secomp Lab at Purdue University 34 Dec 14, 2022