Creating a tfds.load compatible dataset
Hi everyone,What I’m actually trying to do is quite simple but seems poorly documented.All I want is to create an image dataset that I will be able to import through tfds, usingtfds.load().I first tried with the most simple scenario, which is importing the existingcats_vs_dogs datasetand loading it with:train_ds, validation_ds, test_ds = tfds.load(
    "huggingface:microsoft__cats_vs_dogs",
    split=["train[:40%]", "train[40%:50%]", "train[50%:60%]"],
    as_supervised=True,  # Include labels
)And it worked, as expected.I then tried to import this dataset on my personal space with simple piece of code:from datasets import load_dataset

dataset = load_dataset("microsoft/cats_vs_dogs")
dataset.push_to_hub("my_username/cats_vs_dogs")Which resulted in a parquet dataset, as I could expect.My surprise was to find out that this didn’t work:train_ds, validation_ds, test_ds = tfds.load(
    "huggingface:my_username__cats_vs_dogs",
    split=["train[:40%]", "train[40%:50%]", "train[50%:60%]"],
    as_supervised=True,  # Include labels
)I end up an error such as:dataset doesn’t support supervised learning…Also tried to create a dataset fromImageFolder, but I got a different error from pillow: “Impossible to parse ByteIO image” (kinda) when importing the dataset with tfds.What am I missing there ?Any help is appreciated