How to use Image folder

Ran this code
```
 load_dataset("imagefolder", data_dir="./my-dataset")
```

`https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing
```
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
/tmp/ipykernel_33/1648737256.py in <module>
----> 1 load_dataset("imagefolder", data_dir="./my-dataset")

/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
   1684         revision=revision,
   1685         use_auth_token=use_auth_token,
-> 1686         **config_kwargs,
   1687     )
   1688 

/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
   1511         download_config.use_auth_token = use_auth_token
   1512     dataset_module = dataset_module_factory(
-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
   1514     )
   1515 

/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
   1200                         f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
   1201                         f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
-> 1202                     ) from None
   1203                 raise e1 from None
   1204     else:

FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py
```

Labels: name: question

Even this from docs throw same error
```
dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip", split="train")

```

Hi @INF800,

Please note that the `imagefolder` feature enhancement was just recently merged to our master branch (https://github.com/huggingface/datasets/commit/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.

We are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.

In the meantime, you can incorporate that feature if you install our library from our GitHub master branch:
```shell
pip install git+https://github.com/huggingface/datasets#egg=datasets
```

Then:
```python
In [1]: from datasets import load_dataset
        ds = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip", split="train")
Using custom data configuration default-7eb4e80d960deb18
Downloading and preparing dataset image_folder/default to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 690.19it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 852.85it/s]
Dataset image_folder downloaded and prepared to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.

In [2]: ds
Out[2]: 
Dataset({
    features: ['image', 'label'],
    num_rows: 25000
})
```

Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)

Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.

Hey, memory error is resolved. It was fluke.

But there is another issue. Currently `load_dataset("imagefolder", data_dir="./path/to/train",)` takes only `train` as arg to `split` parameter.

I am creating vaildation dataset using

```
ds_valid = datasets.DatasetDict(valid=load_dataset("imagefolder", data_dir="./path/to/valid",)['train'])
```

`data_dir="path/to/folder"` is a shorthand syntax fox `data_files={"train": "path/to/folder/**"}`, so use `data_files` in that case instead:
```python
ds = load_dataset("imagefolder", data_files={"train": "path/to/train/**", "test": "path/to/test/**", "valid": "path/to/valid/**"})
```

And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.

We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:

```python
def to_rgb(batch):
    batch["image"] = [img.convert("RGB") for img in batch["image"]]
    return batch

ds_rgb = ds.map(to_rgb, batched=True)
```

Please use our Forum for questions of this kind in the future.