The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: FileNotFoundError
Message: Couldn't find a dataset script at /src/services/worker/sohonjit/brats2023_multidomain_i2i/brats2023_multidomain_i2i.py or any data file in the same directory. Couldn't find 'sohonjit/brats2023_multidomain_i2i' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in sohonjit/brats2023_multidomain_i2i.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 64, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/sohonjit/brats2023_multidomain_i2i/brats2023_multidomain_i2i.py or any data file in the same directory. Couldn't find 'sohonjit/brats2023_multidomain_i2i' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in sohonjit/brats2023_multidomain_i2i.Need help to make the dataset viewer work? Open a discussion for direct support.
Dataset Summary
This dataset is based on the BraTS2023 dataset and is supposed to be used for Multi-domain Image-to-Image Translation task. It takes 5 middle slices from each nifti volume of the BraTS2023 dataset after normalizing to a value of (-1,1). All of these images are .npy files and one can load them using the np.load(FILEPATH).astype(np.float32). We provide the training and the test set which contains 6255 and 1095 files respectively for each domain. These are actually 4 domains, and are named accordingly.
It is highly recommend to create a separate validation set from the training dataset for applications. We use Pytorch to do this. We do this by using the following command.
seed = 97
train_dataset, val_dataset = torch.utils.data.random_split(
dataset, lengths=(0.9, 0.1), generator=torch.Generator().manual_seed(seed)
) # dataset is the dataset instance.
This dataset is actually part of a paper which is under peer-review currently.
We hope this helps the community.
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