`ImageFolder` performs poorly with large datasets
Hi ! ImageFolder is made for small scale datasets indeed. For large scale image datasets you better group your images in TAR archives or Arrow/Parquet files. This is true not just for ImageFolder loading performance, but also because having millions of files is not ideal for your filesystem or when moving the data around.

Option 1. use TAR archives

I'd suggest you to take a look at how we load [Imagenet](https://huggingface.co/datasets/imagenet-1k/tree/main) for example. The dataset is sharded in multiple TAR archives and there is a [script](https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py) that iterates over the archives to load the images.

Option 2. use Arrow/Parquet

You can load your images as an Arrow Dataset with
```python
from datasets import Dataset, Image, load_from_disk, load_dataset

ds = Dataset.from_dict({"image": list(glob.glob("path/to/dir/**/*.jpg"))})

def add_metadata(example):
    ...

ds = ds.map(add_metadata, num_proc=16)  # num_proc for multiprocessing
ds = ds.cast_column("image", Image())

# save as Arrow locally
ds.save_to_disk("output_dir")
reloaded = load_from_disk("output_dir")

# OR save as Parquet on the HF Hub
ds.push_to_hub("username/dataset_name")
reloaded = load_dataset("username/dataset_name")
# reloaded = load_dataset("username/dataset_name", num_proc=16)  # to use multiprocessing
```

PS: maybe we can actually have something similar to ImageFolder but for image archives at one point ? @lhoestq Thanks!

Perhaps it'd be worth adding a note on the documentation that `ImageFolder` is not intended for large datasets? This limitation is not intuitively obvious to someone who has not used it before, I think. Thanks for the feedback @salieri! I opened #5329 to make it clear `ImageFolder` is not intended for large datasets. Please feel free to comment if you have any other feedback! 🙂 
### Describe the bug

While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.


## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file


## Performance Degradation Point 1

Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85). 

One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance.

As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal.


## Performance Degradation Point 2

The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`. 

It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out.

### Steps to reproduce the bug

```python
from datasets import load_dataset
import os
import huggingface_hub

dataset = load_dataset(
  'imagefolder',
  data_dir='/some/path',
  # just to spell it out:
  split=None,
  drop_labels=True,
  keep_in_memory=False
)

dataset.push_to_hub('account/dataset', private=True)
```

### Expected behavior

While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets.

Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does?

### Environment info

- `datasets` version: 2.7.1
- Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 10.0.1
- Pandas version: 1.3.5

