Loading imagenet-1k validation split takes much more RAM than expected
My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).

Meanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM.
## Describe the bug

Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.


## Steps to reproduce the bug
```python
from datasets import  load_dataset

dataset = load_dataset("imagenet-1k", split="validation")

print(dataset)

"""prints
Dataset({
    features: ['image', 'label'],
    num_rows: 50000
})
"""

pipe_inputs = dataset["image"]
# and wait :-)
```

## Expected results
Use only < 10 GB RAM when loading the images.

## Actual results
![image](https://user-images.githubusercontent.com/9808326/180249183-62f75ca4-d127-402a-9330-f12825a22b0a.png)

```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```

## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80
