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Convert supervision/dataset/core.py docstrings to doctests

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Co-authored-by: copilot-swe-agent[bot] <198982749+copilot@users.noreply.github.com>
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jirkaandcopilot-swe-agent[bot] authored and Jirka Borovec committed 2026-02-02 18:10:37 +09:00
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commit fd9083ae76
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supervision/dataset/core.py
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@@ -165,14 +165,22 @@ class DetectionDataset(BaseDataset):
the training and testing datasets.
Examples:
```python
import supervision as sv
ds = sv.DetectionDataset(...)
train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)
```
>>> import numpy as np
>>> import supervision as sv
>>> ds = sv.DetectionDataset(
... classes=['dog', 'person'],
... images={
... 'img1.jpg': np.zeros((100, 100, 3), dtype=np.uint8),
... 'img2.jpg': np.zeros((100, 100, 3), dtype=np.uint8),
... },
... annotations={
... 'img1.jpg': sv.Detections(xyxy=np.array([[10, 10, 20, 20]])),
... 'img2.jpg': sv.Detections(xyxy=np.array([[30, 30, 40, 40]])),
... }
... )
>>> train_ds, test_ds = ds.split(split_ratio=0.5, random_state=42)
>>> len(train_ds), len(test_ds)
(1, 1)
"""
train_paths, test_paths = train_test_split(
@@ -224,27 +232,31 @@ class DetectionDataset(BaseDataset):
the merged data from the input list.
Examples:
```python
import supervision as sv
ds_1 = sv.DetectionDataset(...)
len(ds_1)
# 100
ds_1.classes
# ['dog', 'person']
ds_2 = sv.DetectionDataset(...)
len(ds_2)
# 200
ds_2.classes
# ['cat']
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
len(ds_merged)
# 300
ds_merged.classes
# ['cat', 'dog', 'person']
```
>>> import numpy as np
>>> import supervision as sv
>>> ds_1 = sv.DetectionDataset(
... classes=['dog', 'person'],
... images={'img1.jpg': np.zeros((100, 100, 3), dtype=np.uint8)},
... annotations={'img1.jpg': sv.Detections.empty()}
... )
>>> len(ds_1)
1
>>> ds_1.classes
['dog', 'person']
>>> ds_2 = sv.DetectionDataset(
... classes=['cat'],
... images={'img2.jpg': np.zeros((100, 100, 3), dtype=np.uint8)},
... annotations={'img2.jpg': sv.Detections.empty()}
... )
>>> len(ds_2)
1
>>> ds_2.classes
['cat']
>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
>>> len(ds_merged)
2
>>> ds_merged.classes
['cat', 'dog', 'person']
"""
def is_in_memory(dataset: DetectionDataset) -> bool:
@@ -748,14 +760,22 @@ class ClassificationDataset(BaseDataset):
the training and testing datasets.
Examples:
```python
import supervision as sv
cd = sv.ClassificationDataset(...)
train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
len(train_cd), len(test_cd)
# (700, 300)
```
>>> import numpy as np
>>> import supervision as sv
>>> cd = sv.ClassificationDataset(
... classes=['cat', 'dog'],
... images={
... 'img1.jpg': np.zeros((100, 100, 3), dtype=np.uint8),
... 'img2.jpg': np.zeros((100, 100, 3), dtype=np.uint8),
... },
... annotations={
... 'img1.jpg': sv.Classifications(class_id=np.array([0])),
... 'img2.jpg': sv.Classifications(class_id=np.array([1])),
... }
... )
>>> train_cd, test_cd = cd.split(split_ratio=0.5, random_state=42)
>>> len(train_cd), len(test_cd)
(1, 1)
"""
train_paths, test_paths = train_test_split(
data=self.image_paths,