Image classification example fails
Cats and dogs example fails.

transformers.__version__
'4.16.2'
datasets.__version__
'1.18.3'

```
python run_image_classification.py \
    --dataset_name cats_vs_dogs \
    --output_dir ./cats_vs_dogs_outputs/ \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --push_to_hub \
    --push_to_hub_model_id vit-base-cats-vs-dogs \
    --learning_rate 2e-4 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 32 \
    --per_device_eval_batch_size 32 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337

Downloading: 3.40kB [00:00, 842kB/s]
Downloading: 2.04kB [00:00, 1.74MB/s]
02/17/2022 15:38:33 - WARNING - datasets.builder - Using custom data configuration default
Downloading and preparing dataset cats_vs_dogs/default (download: 786.68 MiB, generated: 7.16 MiB, post-processed: Unknown size, total: 793.84 MiB) to /Users/juliensimon/.cache/huggingface/datasets/cats_vs_dogs/default/0.0.0/de304955b4952383ceadee4ab96ba2b291f67986bb213efb2255963db39d9ed8...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 825M/825M [00:41<00:00, 19.8MB/s]
Traceback (most recent call last):
  File "/Users/juliensimon/Repos/transformers/examples/pytorch/image-classification/run_image_classification.py", line 352, in <module>
    main()
  File "/Users/juliensimon/Repos/transformers/examples/pytorch/image-classification/run_image_classification.py", line 204, in main
    ds = load_dataset(
  File "/usr/local/anaconda3/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
    builder_instance.download_and_prepare(
  File "/usr/local/anaconda3/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
    self._download_and_prepare(
  File "/usr/local/anaconda3/lib/python3.9/site-packages/datasets/builder.py", line 695, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "/usr/local/anaconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=7503250, num_examples=23422, dataset_name='cats_vs_dogs'), 'recorded': SplitInfo(name='train', num_bytes=7871070, num_examples=23410, dataset_name='cats_vs_dogs')}]
```

Hi,

I wasn't able to reproduce your issue. It runs fine for me in Colab. Final log:

```
{'eval_loss': 0.08317019045352936, 'eval_accuracy': 0.9774436090225563, 'eval_runtime': 1.9557, 'eval_samples_per_second': 68.008, 'eval_steps_per_second': 8.693, 'epoch': 5.0}

100% 650/650 [02:58<00:00,  4.25it/s]
                                   [INFO|trainer.py:2137] 2022-02-17 17:56:40,728 >> Saving model checkpoint to ./beans_outputs/checkpoint-650
[INFO|configuration_utils.py:439] 2022-02-17 17:56:40,729 >> Configuration saved in ./beans_outputs/checkpoint-650/config.json
[INFO|modeling_utils.py:1084] 2022-02-17 17:56:41,509 >> Model weights saved in ./beans_outputs/checkpoint-650/pytorch_model.bin
[INFO|feature_extraction_utils.py:352] 2022-02-17 17:56:41,510 >> Feature extractor saved in ./beans_outputs/checkpoint-650/preprocessor_config.json
[INFO|trainer.py:2215] 2022-02-17 17:56:44,280 >> Deleting older checkpoint [beans_outputs/checkpoint-260] due to args.save_total_limit
[INFO|trainer.py:1506] 2022-02-17 17:56:44,315 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)
```

Notebook I used is [here](https://colab.research.google.com/drive/1Z2Mv9TbbddydwPblQT-sq4lzFncpmGYU?usp=sharing).<|||||>Update: I see you're using the cats and dogs dataset instead of beans. I'll rerun.

Update v2: This is not a Transformers issue, but a Datasets issue. A minimal reproducer is the following:

```
from datasets import load_dataset 

dataset = load_dataset("cats_vs_dogs")
```<|||||>I think this was resolved in the `datasets` upstream!<|||||>Indeed, therefore closing this PR. For now, you have to install datasets from master to have it.<|||||>OK, thank you.