- [MINOR] [CONFIG] [UPDATE] 1. update README.md

README copy.md

@@ -1,163 +0,0 @@
-# COVER
-
-Official Code for [CVPR Workshop2024] Paper *"COVER: A Comprehensive Video Quality Evaluator"*. 
-Official Code, Demo, Weights for the [Comprehensive Video Quality Evaluator (COVER)].
-
-# Todo:: update date, hugging face model below
-- xx xxx, 2024: We upload weights of [COVER](https://github.com/vztu/COVER/release/Model/COVER.pth) and [COVER++](TobeContinue) to Hugging Face models.
-- xx xxx, 2024: We upload Code of [COVER](https://github.com/vztu/COVER)
-- 12 Apr, 2024: COVER has been accepted by CVPR Workshop2024.
-
-
-# Todo:: update [visitors](link) below
-![visitors](https://visitor-badge.laobi.icu/badge?page_id=teowu/TobeContinue) [![](https://img.shields.io/github/stars/vztu/COVER)](https://github.com/vztu/COVER)
-[![State-of-the-Art](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/QualityAssessment/COVER)
-<a href="https://colab.research.google.com/github/taskswithcode/COVER/blob/master/TWCCOVER.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> 
-
-
-# Todo:: update predicted score for YT-UGC challenge dataset specified by AIS
-**COVER** Pseudo-labelled Quality scores of [YT-UGC](https://www.deepmind.com/open-source/kinetics): [CSV](https://github.com/QualityAssessment/COVER/raw/master/cover_predictions/kinetics_400_1.csv)
-
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/disentangling-aesthetic-and-technical-effects/video-quality-assessment-on-youtube-ugc)](https://paperswithcode.com/sota/video-quality-assessment-on-youtube-ugc?p=disentangling-aesthetic-and-technical-effects)
-
-
-## Introduction
-#  Todo:: Add Introduction here
-
-### the proposed COVER
-
-*This inspires us to*
-
-![Fig](figs/approach.png)
-
-## Install
-
-The repository can be installed via the following commands:
-```shell
-git clone https://github.com/vztu/COVER 
-cd COVER 
-pip install -e . 
-mkdir pretrained_weights 
-cd pretrained_weights 
-wget https://github.com/vztu/COVER/release/Model/COVER.pth
-cd ..
-```
-
-
-## Evaluation: Judge the Quality of Any Video
-
-### Try on Demos
-You can run a single command to judge the quality of the demo videos in comparison with videos in VQA datasets. 
-
-```shell
-    python evaluate_one_video.py -v ./demo/video_1.mp4
-```
-
-or 
-
-```shell
-    python evaluate_one_video.py -v ./demo/video_2.mp4
-```
-
-Or choose any video you like to predict its quality:
-
-
-```shell
-    python evaluate_one_video.py -v $YOUR_SPECIFIED_VIDEO_PATH$
-```
-
-### Outputs
-
-#### ITU-Standarized Overall Video Quality Score
-
-The script can directly score the video's overall quality (considering all perspectives).
-
-```shell
-    python evaluate_one_video.py -v $YOUR_SPECIFIED_VIDEO_PATH$
-```
-
-The final output score is averaged among all perspectives.
-
-
-## Evaluate on a Exsiting Video Dataset
-
-
-```shell
-    python evaluate_one_dataset.py -in $YOUR_SPECIFIED_DIR$ -out $OUTPUT_CSV_PATH$
-```
-
-## Evaluate on a Set of Unlabelled Videos
-
-
-```shell
-    python evaluate_a_set_of_videos.py -in $YOUR_SPECIFIED_DIR$ -out $OUTPUT_CSV_PATH$
-```
-
-The results are stored as `.csv` files in cover_predictions in your `OUTPUT_CSV_PATH`.
-
-Please feel free to use COVER to pseudo-label your non-quality video datasets.
-
-
-## Data Preparation
-
-We have already converted the labels for most popular datasets you will need for Blind Video Quality Assessment,
-and the download links for the **videos** are as follows:
-
-:book: LSVQ: [Github](https://github.com/baidut/PatchVQ)
-
-:book: KoNViD-1k: [Official Site](http://database.mmsp-kn.de/konvid-1k-database.html)
-
-:book: LIVE-VQC: [Official Site](http://live.ece.utexas.edu/research/LIVEVQC)
-
-:book: YouTube-UGC: [Official Site](https://media.withyoutube.com)
-
-*(Please contact the original authors if the download links were unavailable.)*
-
-After downloading, kindly put them under the `../datasets` or anywhere but remember to change the `data_prefix` respectively in the [config file](cover.yml).
-
-# Training: Adapt COVER to your video quality dataset!
-
-Now you can employ ***head-only/end-to-end transfer*** of COVER to get dataset-specific VQA prediction heads. 
-
-We still recommend **head-only** transfer. As we have evaluated in the paper, this method has very similar performance with *end-to-end transfer* (usually 1%~2% difference), but will require **much less** GPU memory, as follows:
-
-```shell
-    python transfer_learning.py -t $YOUR_SPECIFIED_DATASET_NAME$
-```
-
-For existing public datasets, type the following commands for respective ones:
-
-- `python transfer_learning.py -t val-kv1k` for KoNViD-1k.
-- `python transfer_learning.py -t val-ytugc` for YouTube-UGC.
-- `python transfer_learning.py -t val-cvd2014` for CVD2014.
-- `python transfer_learning.py -t val-livevqc` for LIVE-VQC.
-
-
-As the backbone will not be updated here, the checkpoint saving process will only save the regression heads with only `398KB` file size (compared with `200+MB` size of the full model). To use it, simply replace the head weights with the official weights [COVER.pth](https://github.com/vztu/COVER/release/Model/COVER.pth).
-
-We also support ***end-to-end*** fine-tune right now (by modifying the `num_epochs: 0` to `num_epochs: 15` in `./cover.yml`). It will require more memory cost and more storage cost for the weights (with full parameters) saved, but will result in optimal accuracy.
-
-Fine-tuning curves by authors can be found here: [Official Curves](https://wandb.ai/timothyhwu/COVER) for reference.
-
-
-## Visualization
-
-### WandB Training and Evaluation Curves
-
-You can be monitoring your results on WandB!
-
-## Acknowledgement
-
-Thanks for every participant of the subjective studies!
-
-## Citation
-
-Should you find our work interesting and would like to cite it, please feel free to add these in your references! 
-
-
-# Todo, add bibtex of cover below
-```bibtex
-%cover
-
-```

README.md

@@ -11,3 +11,167 @@ license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# COVER
+
+Official Code for [CVPR Workshop2024] Paper *"COVER: A Comprehensive Video Quality Evaluator"*. 
+Official Code, Demo, Weights for the [Comprehensive Video Quality Evaluator (COVER)].
+
+# Todo:: update date, hugging face model below
+- xx xxx, 2024: We upload weights of [COVER](https://github.com/vztu/COVER/release/Model/COVER.pth) and [COVER++](TobeContinue) to Hugging Face models.
+- xx xxx, 2024: We upload Code of [COVER](https://github.com/vztu/COVER)
+- 12 Apr, 2024: COVER has been accepted by CVPR Workshop2024.
+
+
+# Todo:: update [visitors](link) below
+![visitors](https://visitor-badge.laobi.icu/badge?page_id=teowu/TobeContinue) [![](https://img.shields.io/github/stars/vztu/COVER)](https://github.com/vztu/COVER)
+[![State-of-the-Art](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/QualityAssessment/COVER)
+<a href="https://colab.research.google.com/github/taskswithcode/COVER/blob/master/TWCCOVER.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> 
+
+
+# Todo:: update predicted score for YT-UGC challenge dataset specified by AIS
+**COVER** Pseudo-labelled Quality scores of [YT-UGC](https://www.deepmind.com/open-source/kinetics): [CSV](https://github.com/QualityAssessment/COVER/raw/master/cover_predictions/kinetics_400_1.csv)
+
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/disentangling-aesthetic-and-technical-effects/video-quality-assessment-on-youtube-ugc)](https://paperswithcode.com/sota/video-quality-assessment-on-youtube-ugc?p=disentangling-aesthetic-and-technical-effects)
+
+
+## Introduction
+#  Todo:: Add Introduction here
+
+### the proposed COVER
+
+*This inspires us to*
+
+![Fig](figs/approach.png)
+
+## Install
+
+The repository can be installed via the following commands:
+```shell
+git clone https://github.com/vztu/COVER 
+cd COVER 
+pip install -e . 
+mkdir pretrained_weights 
+cd pretrained_weights 
+wget https://github.com/vztu/COVER/release/Model/COVER.pth
+cd ..
+```
+
+
+## Evaluation: Judge the Quality of Any Video
+
+### Try on Demos
+You can run a single command to judge the quality of the demo videos in comparison with videos in VQA datasets. 
+
+```shell
+    python evaluate_one_video.py -v ./demo/video_1.mp4
+```
+
+or 
+
+```shell
+    python evaluate_one_video.py -v ./demo/video_2.mp4
+```
+
+Or choose any video you like to predict its quality:
+
+
+```shell
+    python evaluate_one_video.py -v $YOUR_SPECIFIED_VIDEO_PATH$
+```
+
+### Outputs
+
+#### ITU-Standarized Overall Video Quality Score
+
+The script can directly score the video's overall quality (considering all perspectives).
+
+```shell
+    python evaluate_one_video.py -v $YOUR_SPECIFIED_VIDEO_PATH$
+```
+
+The final output score is averaged among all perspectives.
+
+
+## Evaluate on a Exsiting Video Dataset
+
+
+```shell
+    python evaluate_one_dataset.py -in $YOUR_SPECIFIED_DIR$ -out $OUTPUT_CSV_PATH$
+```
+
+## Evaluate on a Set of Unlabelled Videos
+
+
+```shell
+    python evaluate_a_set_of_videos.py -in $YOUR_SPECIFIED_DIR$ -out $OUTPUT_CSV_PATH$
+```
+
+The results are stored as `.csv` files in cover_predictions in your `OUTPUT_CSV_PATH`.
+
+Please feel free to use COVER to pseudo-label your non-quality video datasets.
+
+
+## Data Preparation
+
+We have already converted the labels for most popular datasets you will need for Blind Video Quality Assessment,
+and the download links for the **videos** are as follows:
+
+:book: LSVQ: [Github](https://github.com/baidut/PatchVQ)
+
+:book: KoNViD-1k: [Official Site](http://database.mmsp-kn.de/konvid-1k-database.html)
+
+:book: LIVE-VQC: [Official Site](http://live.ece.utexas.edu/research/LIVEVQC)
+
+:book: YouTube-UGC: [Official Site](https://media.withyoutube.com)
+
+*(Please contact the original authors if the download links were unavailable.)*
+
+After downloading, kindly put them under the `../datasets` or anywhere but remember to change the `data_prefix` respectively in the [config file](cover.yml).
+
+# Training: Adapt COVER to your video quality dataset!
+
+Now you can employ ***head-only/end-to-end transfer*** of COVER to get dataset-specific VQA prediction heads. 
+
+We still recommend **head-only** transfer. As we have evaluated in the paper, this method has very similar performance with *end-to-end transfer* (usually 1%~2% difference), but will require **much less** GPU memory, as follows:
+
+```shell
+    python transfer_learning.py -t $YOUR_SPECIFIED_DATASET_NAME$
+```
+
+For existing public datasets, type the following commands for respective ones:
+
+- `python transfer_learning.py -t val-kv1k` for KoNViD-1k.
+- `python transfer_learning.py -t val-ytugc` for YouTube-UGC.
+- `python transfer_learning.py -t val-cvd2014` for CVD2014.
+- `python transfer_learning.py -t val-livevqc` for LIVE-VQC.
+
+
+As the backbone will not be updated here, the checkpoint saving process will only save the regression heads with only `398KB` file size (compared with `200+MB` size of the full model). To use it, simply replace the head weights with the official weights [COVER.pth](https://github.com/vztu/COVER/release/Model/COVER.pth).
+
+We also support ***end-to-end*** fine-tune right now (by modifying the `num_epochs: 0` to `num_epochs: 15` in `./cover.yml`). It will require more memory cost and more storage cost for the weights (with full parameters) saved, but will result in optimal accuracy.
+
+Fine-tuning curves by authors can be found here: [Official Curves](https://wandb.ai/timothyhwu/COVER) for reference.
+
+
+## Visualization
+
+### WandB Training and Evaluation Curves
+
+You can be monitoring your results on WandB!
+
+## Acknowledgement
+
+Thanks for every participant of the subjective studies!
+
+## Citation
+
+Should you find our work interesting and would like to cite it, please feel free to add these in your references! 
+
+
+# Todo, add bibtex of cover below
+```bibtex
+%cover
+
+```