JLB-JLB/seizure_eeg_train

---
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"

```python
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'])

```

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

原始信息汇总

数据集概述

数据集信息

• 特征:

  • image: 图像数据
  • epoch: 整数类型
  • label_str: 分类标签，包含以下类别:
    • 0: No Event
    • 1: bckg
    • 2: seiz
  • label: 分类标签，包含以下类别:
    • 0: No Event
    • 1: bckg
    • 2: seiz

• 分割:

  • train: 训练集，包含814568个样本，总大小为23742147634.792字节

• 大小:

  • 下载大小: 24165936927字节
  • 数据集大小: 23742147634.792字节

配置

• 默认配置:
  • 数据文件路径: data/train-*

使用示例

python from datasets import load_dataset

dataset_name = "JLB-JLB/seizure_eeg_train"

dataset = load_dataset( dataset_name, split="train", )

创建训练和测试/验证分割

train_testvalid = dataset.train_test_split(test_size=0.1, shuffle=True, seed=12071998)

获取训练、测试和验证集中的不同标签数量

display(train_testvalid["train"].features["label"]) display(train_testvalid["test"].features["label"].num_classes)

检查标签/类别数量

num_classes = len(set(train_testvalid["train"][label])) labels = train_testvalid["train"].features[label] print(num_classes, labels)

显示训练集中的第一个图像

display(train_testvalid["train"][0][image])