| import mlcroissant as mlc | |
| import pandas as pd | |
| import os | |
| # These values are used by mlcroissant | |
| # to perform the necessary auth to fetch the data | |
| # Provide your Kaggle username and API key | |
| os.environ['CROISSANT_BASIC_AUTH_USERNAME'] = 'enzetao' | |
| os.environ['CROISSANT_BASIC_AUTH_PASSWORD'] = "a97ddf556baf4db4723d78aab014b599" | |
| # Fetch the Croissant JSON-LD | |
| # croissant_dataset = mlc.Dataset('https://www.kaggle.com/datasets/yueyin27/refref/croissant/download') | |
| # croissant_dataset = mlc.Dataset('https://www.kaggle.com/datasets/nguyenhung1903/nerf-synthetic-dataset/croissant/download') | |
| # croissant_dataset = mlc.Dataset('https://www.kaggle.com/datasets/enzetao/refref/croissant/download') | |
| # croissant_dataset = mlc.Dataset('https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset/croissant/download') | |
| url = 'https://huggingface.co/api/datasets/fashion_mnist/croissant' | |
| print(mlc.Dataset(url).metadata.to_json()) | |
| import tensorflow_datasets as tfds | |
| builder = tfds.core.dataset_builders.CroissantBuilder( | |
| jsonld=url, | |
| record_set_ids=["fashion_mnist"], | |
| file_format='array_record', | |
| ) | |
| builder.download_and_prepare() | |
| # # Check what record sets are in the dataset | |
| # record_sets = croissant_dataset.metadata.record_sets | |
| # print(record_sets) | |
| # # Fetch the records and put them in a DataFrame | |
| # record_set_df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid)) | |
| # record_set_df.head() |