docs: 🗃️ remove autodownload of metainfo and put them to huggingface

Author: chaofengc

README.md

@@ -136,26 +136,34 @@ Please refer to the [results calibration](./ResultsCalibra/ResultsCalibra.md) to
 
 ### ⏬ Download Benchmark Datasets
 
-For convenience, we upload all related datasets to [huggingface](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets/tree/main). 
-Here are example codes to download datasets from huggingface:
+For convenience, we upload all related datasets to [huggingface IQA-Toolbox-Dataset](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets), and corresponding meta information files to [huggingface IQA-Toolbox-Dataset-metainfo](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets-metainfo). 
+Here are example codes to download them from huggingface:
 
 >[!CAUTION]
 > we only collect the datasets for academic, research, and educational purposes. It is important for the users to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset.
 
-
 ```python
 import os
 from huggingface_hub import snapshot_download
 
 save_dir = './datasets'
 os.makedirs(save_dir, exist_ok=True)
 
-filename = "meta_info.tgz"
+filename = "koniq10k.tgz"
 snapshot_download("chaofengc/IQA-Toolbox-Datasets", repo_type="dataset", local_dir=save_dir, allow_patterns=filename, local_dir_use_symlinks=False)
 
 os.system(f"tar -xzvf {save_dir}/{filename} -C {save_dir}")
 ```
 
+Download meta information from Huggingface with `git clone` or update with `git pull`:
+```
+cd ./datasets
+git clone https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets-metainfo meta_info
+
+cd ./datasets/meta_info
+git pull
+```
+
 Examples to specific dataset options can be found in `./options/default_dataset_opt.yml`. Details of the dataloader interface and meta information files can be found in [Dataset Preparation](docs/Dataset_Preparation.md)
 
 ### Performance Evaluation Protocol

pyiqa/train.py

@@ -31,14 +31,6 @@ def init_tb_loggers(opt):
 
 
 def create_train_val_dataloader(opt, logger):
-    # download meta informatioin for datasets if needed
-    if not os.path.exists(f"{opt['root_path']}/datasets/meta_info"):
-        logger.info('Downloading meta information for datasets.')
-        os.makedirs(f"{opt['root_path']}/datasets", exist_ok=True)
-        file_path = load_file_from_url('https://github.com/chaofengc/IQA-PyTorch/releases/download/v0.1-weights/meta_info.tgz', f"{opt['root_path']}/datasets")
-        metainfo_file = tarfile.open(file_path, mode='r|gz')
-        metainfo_file.extractall(f"{opt['root_path']}/datasets")
-
     # create train and val dataloaders
     train_loader, val_loaders = None, []
     for phase, dataset_opt in opt['datasets'].items():