metadata
dataset_info:
features:
- name: image
dtype: image
- name: epoch
dtype: int64
- name: label_str
dtype:
class_label:
names:
'0': No Event
'1': bckg
'2': seiz
- name: label
dtype:
class_label:
names:
'0': No Event
'1': bckg
'2': seiz
splits:
- name: train
num_bytes: 23742147634.792
num_examples: 814568
download_size: 24165936927
dataset_size: 23742147634.792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Card for "seizure_eeg_train"
from datasets import load_dataset
dataset_name = "JLB-JLB/seizure_eeg_train"
dataset = load_dataset(
dataset_name,
split="train",
)
display(dataset)
# create train and test/val split
train_testvalid = dataset.train_test_split(test_size=0.1, shuffle=True, seed=12071998)
display(train_testvalid)
# get the number of different labels in the train, test and validation set
display(train_testvalid["train"].features["label"])
display(train_testvalid["test"].features["label"].num_classes)
# check how many labels/number of classes
num_classes = len(set(train_testvalid["train"]['label']))
labels = train_testvalid["train"].features['label']
print(um_classes, labels)
display(train_testvalid["train"][0]['image'])