image
imagewidth (px) 224
224
| epoch
int64 0
2.57k
| label_str
class label 3
classes | label
class label 3
classes |
---|---|---|---|
0 | 0No Event
| 0No Event
|
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1 | 0No Event
| 0No Event
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2 | 0No Event
| 0No Event
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3 | 0No Event
| 0No Event
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4 | 0No Event
| 0No Event
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5 | 0No Event
| 0No Event
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6 | 0No Event
| 0No Event
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7 | 0No Event
| 0No Event
|
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8 | 2seiz
| 2seiz
|
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9 | 2seiz
| 2seiz
|
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10 | 2seiz
| 2seiz
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11 | 2seiz
| 2seiz
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12 | 2seiz
| 2seiz
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13 | 2seiz
| 2seiz
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14 | 2seiz
| 2seiz
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15 | 2seiz
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16 | 2seiz
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17 | 2seiz
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18 | 2seiz
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19 | 2seiz
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20 | 2seiz
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21 | 2seiz
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22 | 2seiz
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23 | 2seiz
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24 | 2seiz
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25 | 2seiz
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26 | 2seiz
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27 | 2seiz
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28 | 2seiz
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29 | 2seiz
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30 | 2seiz
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31 | 2seiz
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32 | 2seiz
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33 | 2seiz
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34 | 2seiz
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35 | 2seiz
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36 | 2seiz
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37 | 2seiz
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38 | 2seiz
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39 | 2seiz
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40 | 2seiz
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41 | 2seiz
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42 | 2seiz
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43 | 2seiz
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44 | 2seiz
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45 | 2seiz
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46 | 2seiz
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47 | 2seiz
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48 | 2seiz
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49 | 2seiz
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50 | 2seiz
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51 | 2seiz
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52 | 2seiz
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53 | 2seiz
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54 | 2seiz
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55 | 2seiz
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56 | 2seiz
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57 | 2seiz
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58 | 2seiz
| 2seiz
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59 | 2seiz
| 2seiz
|
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60 | 0No Event
| 0No Event
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61 | 0No Event
| 0No Event
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62 | 0No Event
| 0No Event
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63 | 0No Event
| 0No Event
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64 | 0No Event
| 0No Event
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65 | 0No Event
| 0No Event
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66 | 0No Event
| 0No Event
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67 | 0No Event
| 0No Event
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68 | 0No Event
| 0No Event
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69 | 0No Event
| 0No Event
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70 | 0No Event
| 0No Event
|
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71 | 0No Event
| 0No Event
|
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72 | 0No Event
| 0No Event
|
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73 | 0No Event
| 0No Event
|
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0 | 0No Event
| 0No Event
|
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1 | 0No Event
| 0No Event
|
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2 | 2seiz
| 2seiz
|
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3 | 2seiz
| 2seiz
|
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4 | 2seiz
| 2seiz
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5 | 2seiz
| 2seiz
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6 | 2seiz
| 2seiz
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7 | 2seiz
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8 | 2seiz
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9 | 2seiz
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10 | 2seiz
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11 | 2seiz
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12 | 2seiz
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13 | 2seiz
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14 | 2seiz
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15 | 2seiz
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16 | 2seiz
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17 | 2seiz
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18 | 2seiz
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19 | 2seiz
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20 | 2seiz
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21 | 2seiz
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22 | 2seiz
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23 | 2seiz
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24 | 2seiz
| 2seiz
|
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25 | 2seiz
| 2seiz
|
End of preview. Expand
in Dataset Viewer.
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'])
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