FAST-VQA/FasterVQA

📘We are holding an unofficial copy of the LSVQ dataset on huggingface Link, which is necessary to reproduce training for FAST-VQA/FasterVQA. Please comply with the original terms of use of this dataset, as we hold no responsibilities on any non-compliant usage.

visitors State-of-the-Art

Performances for FasterVQA:

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Performances for FAST-VQA:

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An Open Source Deep End-to-End Video Quality Assessment Toolbox,

开源的端到端视频质量评价工具箱，

& Reproducible Code for ECCV2022 Paper FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling and its extension Paper Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment.

暨 可复现 ECCV2022 论文 FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling 的代码。

:sparkles: We are officially announcing FasterVQA (named FAST-VQA-B-3D during development) which expands the proposed Fragments into a 3D version, which brings 4x faster speed and similar performance. The official CVF edition of ECCV paper will also be online soon as the conference is coming.

我们正式发布了新版的FasterVQA，在效率提升4倍的情况下保持接近原始FAST-VQA的性能。

In this release, we have refactored the training and testing code. The refactored code can achieve the same performance as the original version and allow modification of (1) the backbone structures; (2) the sampling hyper-parameters; (3) loss functions.

在这一版本中，我们对训练和测试的代码进行了重构。重构后的代码可以达到与原始版本相同的性能，并允许修改网络结构/采样的超参数/损失函数。

Fig

[Upgraded] Infer for a single MP4 video

python vqa.py -m [MODEL_TYPE] -v [YOUR_INPUT_FILE_PATH]

MODEL_TYPE can be chosen in FasterVQA, FAST-VQA, and their efficient versions, FasterVQA-MS, FasterVQA-MT, FAST-VQA-M.

Outputs for default python vqa.py (inferring with FasterVQA) can be:

Inferring with model [FasterVQA]:
The quality score of the video (range [0,1]) is 0.42326.

Outputs for python vqa.py -m FAST-VQA (inferring with FAST-VQA) can be:

Inferring with model [FAST-VQA]:
The quality score of the video (range [0,1]) is 0.44953.

The result is now rescaled into between [0,1] with a sigmoid function.

Score near 0: extremely bad quality..

Score 0.25: bad quality.

Score 0.5: fair quality.

Score 0.75: good quality.

Score near 1.0: extremely good quality.

See our Weights & Biases training logs

我们在Wandb上公开了一部分训练和测试曲线。

We are reproducing several experiments and making public our training logs here.

传送门/Gate to Curves

Now supports:

:triangular_flag_on_post: Modularized Parts Designed for Development

为开发设计的模块化架构

Data Preprocessing

数据预处理

Please view Data Processing to see the source codes for data processing. Specially, look at the FusionDataset class and the get_spatial_and_temporal_samples function for our core transformations.

Spatial Sampling

空间采样

We have supported spatial sampling approachs as follows:

We also support the combination of those sampling approaches (multi-branch networks) for more flexibility.

Temporal Sampling (New)

时域采样（新）

We also support different temporal sampling approaches:

Network Structures

网络结构

Network Backbones

骨干网络

Network Heads

网络头

IP-NLR head can generate local quality maps for videos.

Installation

安装

Dependencies

依赖

The original library is build with

while using decord module to read original videos (so that you don’t need to make any transform on your original .mp4 input).

To get all the requirements, please run

pip install -r requirements.txt

Direct Install

直接安装

You can run

git clone htps://github.com/QualityAssessment/FAST-VQA-and-FasterVQA.git
cd FAST-VQA-and-FasterVQA
pip install -e .

to install the full FAST-VQA with its requirements. The -e option allows you to import your customized version of the package.

Usage

使用方法

Quick Benchmark

快速测试

Step 1: Get Pretrained Weights

We supported pretrained weights for several versions:

Name Pretrain Spatial Fragments Temporal Fragments PLCC@LSVQ_1080p PLCC@LSVQ_test PLCC@LIVE_VQC PLCC@KoNViD MACs config model
FAST-VQA-B (ECCV2022) Kinetics-400 7*32 1*32*(4) 0.814 0.877 0.844 0.855 279G config github
FasterVQA (:sparkles: New!) Kinetics-400 7*32 84(1) 0.811 0.874 0.837 0.864 69G config github
- zero-shot transfer to MT scale with AMI Kinetics-400 7*32 44(1) 0.791 0.860 0.826 0.849 35G config Same as FasterVQA
- zero-shot transfer to MS scale with AMI Kinetics-400 5*32 84(1) 0.798 0.849 0.818 0.854 36G config Same as FasterVQA
FAST-VQA-B-From-Scratch (:sparkles: New!) None 7*32 132(4) 0.707 0.791 0.766 0.793 279G config github
FAST-VQA-B-3D-From-Scratch (:sparkles: New!) None 7*32 84(1) 0.685 0.760 0.739 0.773 69G config github
FAST-VQA-M (ECCV2022) Kinetics-400 4*32 1*32(*4) 0.773 0.854 0.810 0.832 46G config github

Step 2: Download Corresponding Datasets

LSVQ: Github KoNViD-1k: Official Site LIVE-VQC: Official Site

Step 3: Run the following one-line script!

python new_test.py -o [YOUR_OPTIONS]

Training

训练

Get Pretrained Weights from Recognition

You might need to download the original Swin-T Weights to initialize the model.

Train with large dataset (LSVQ)

To train FAST-VQA-B, please run

python new_train.py -o options/fast/fast-b.yml

To train FAST-VQA-M, please run

python new_train.py -o options/fast/fast-m.yml

To train FasterVQA (FAST-VQA-B-3D), please run

python new_train.py -o options/fast/f3dvqa-b.yml

Finetune on small datasets with provided weights

在小规模数据集上进行调优

This training will split the dataset into 10 random train/test splits (with random seed 42) and report the best result on the random split of the test dataset.

python split_train.py -opt [YOUR_OPTION_FILE]

You may see option files in Finetune Config Files.

Results for FAST-VQA-B:

  KoNViD-1k CVD2014 LIVE-Qualcomm LIVE-VQC YouTube-UGC
SROCC 0.891 0.891 0.819 0.849 0.855
PLCC 0.892 0.903 0.851 0.862 0.852

Results for FasterVQA(FAST-VQA-B-3D):

  KoNViD-1k CVD2014 LIVE-Qualcomm LIVE-VQC YouTube-UGC
SROCC 0.895 0.896 0.826 0.843 0.863
PLCC 0.898 0.904 0.843 0.858 0.859

Note that this part only support FAST-VQA-B and FAST-VQA-B-3D (FasterVQA); but you may build your own option files for other variants.

Supported datasets are KoNViD-1k, LIVE_VQC, CVD2014, LIVE-Qualcomm, YouTube-UGC.

Citation

The following papers are to be cited in the bibliography if relevant papers are proposed.

@misc{wu2022fasterquality,
      title={Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment}, 
      author={Haoning Wu and Chaofeng Chen and Liang Liao and Jingwen Hou and Wenxiu Sun and Qiong Yan and Jinwei Gu and Weisi Lin},
      year={2022},
      eprint={2210.05357},
      archivePrefix={arXiv}
}

@article{wu2022fastquality,
  title={FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling},
  author={Wu, Haoning and Chen, Chaofeng and Hou, Jingwen and Liao, Liang and Wang, Annan and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={Proceedings of European Conference of Computer Vision (ECCV)},
  year={2022}
}

And this code library if it is used.

@misc{end2endvideoqualitytool,
  title = {Open Source Deep End-to-End Video Quality Assessment Toolbox},
  author = {Wu, Haoning},
  year = {2022},
  url = {http://github.com/timothyhtimothy/fast-vqa}
}