- 12 Oct, 2022: Release of pre-print FasterVQA paper: PDF, Abstract.
- 27 Sep, 2022: Release of FasterVQA models: 4X more efficient, 14X real-time inference on Apple M1 CPU (for FasterVQA-MT, tested on my old Mac).
- 10 Sep, 2022: Support on Adaptive Multi-scale Inference (AMI): one model for different scales of inputs.
Performances for FasterVQA:
Performances for FAST-VQA:
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。
✨ 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。
python vqa.py -d [YOUR_INPUT_FILE_PATH]
The default one is for a video in KoNViD-1k with FasterVQA, which should get a score around 0.133.
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.
https://wandb.ai/timothyhwu/Open_FAST_VQA
Now supports:
- FasterVQA-finetuned-to-KonViD-1k
- FasterVQA-on-MT-and-MS-scales-with-AMI
为开发设计的模块化架构
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.
空间采样
We have supported spatial sampling approachs as follows:
- fragments
- resize
- arp_resize (resize while keeping the original Aspect Ratio)
- crop
We also support the combination of those sampling approaches (multi-branch networks) for more flexibility.
时域采样（新）
We also support different temporal sampling approaches:
- SampleFrames (sample continuous frames, imported from MMAction2)
- FragmentSampleFrames (:sparkles: New, sample fragment-like discontinuous frames)
网络结构
骨干网络
- Video Swin Transformer (with GRPB, as proposed in FAST-VQA)
- Video Swin Transformer (vanilla)
- ConvNext-I3D (vanilla)
网络头
- IP-NLR Head (as proposed in FAST-VQA)
IP-NLR head can generate local quality maps for videos.
安装
依赖
The original library is build with
- python=3.8.8
- torch=1.10.2
- torchvision=0.11.3
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直接安装
You can run
pip install .or
python setup.py installlto install the full FAST-VQA with its requirements.
使用方法
快速测试
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 | 8*4(*1) | 0.811 | 0.874 | 0.837 | 0.864 | 69G | config | github |
| - zero-shot transfer to MT scale with AMI | Kinetics-400 | 7*32 | 4*4(*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 | 8*4(*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 | 8*4(*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 |
LSVQ: Github KoNViD-1k: Official Site LIVE-VQC: Official Site
python new_test.py -o [YOUR_OPTIONS]
训练
You might need to download the original Swin-T Weights to initialize the model.
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
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.
The following paper is to be cited in the bibliography if relevant papers are proposed.
@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}
}