file_name
stringlengths 23
23
| image
unknown | video_id
stringclasses 1
value | frame_number
int64 0
31.7k
| timestamp
float64 0
529
| road_type
stringclasses 2
values | weather
stringclasses 2
values | scene
stringclasses 2
values | has_car
int64 1
1
| has_motorcycle
int64 0
1
| has_truck
int64 0
1
| has_bus
int64 0
1
| has_pedestrian
int64 0
1
| has_bicycle
int64 0
0
| has_traffic_light
int64 0
1
| has_traffic_sign
int64 0
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GX012073_frame_0000.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 0
| 0
|
rural
|
clear
|
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| 1
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GX012073_frame_0001.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 299
| 4.988317
|
rural
|
clear
|
highway
| 1
| 0
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|
GX012073_frame_0002.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 598
| 9.976633
|
highway
|
clear
|
other
| 1
| 0
| 0
| 0
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| 0
| 0
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|
GX012073_frame_0003.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 897
| 14.96495
|
rural
|
clear
|
other
| 1
| 0
| 1
| 0
| 0
| 0
| 0
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|
GX012073_frame_0004.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 1,196
| 19.953267
|
highway
|
clear
|
other
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
GX012073_frame_0005.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 1,495
| 24.941583
|
rural
|
clear
|
other
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
GX012073_frame_0006.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 1,794
| 29.9299
|
highway
|
clear
|
other
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
GX012073_frame_0007.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 2,093
| 34.918217
|
highway
|
clear
|
other
| 1
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| 1
| 0
| 0
| 0
| 0
| 0
|
GX012073_frame_0008.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 2,392
| 39.906533
|
highway
|
clear
|
other
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
GX012073_frame_0009.jpg
| "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
|
GX012073
| 2,691
| 44.89485
|
highway
|
clear
|
other
| 1
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| 0
|
End of preview. Expand
in Data Studio
DriveThruThailand Labeled Dataset
Dataset Description
A collection of 107 screenshots from DriveThruThailand videos with labels.
Features
Each image includes the following features:
- file_name: File name
- image_path: Path to the image within the dataset
- video_id: Source video ID
- frame_number: Frame number in the source video
- timestamp: Timestamp in seconds
- road_type: Road type classification (highway, urban)
- weather: Weather condition (clear, cloudy)
- scene: Scene type (city, countryside)
File Structure
/images/: Contains all the imagesmetadata.csv: Contains metadata for all images
Usage
This dataset can be used with Hugging Face's datasets library:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("notewee/drivethruthailand-labeled")
# Access metadata
metadata = pd.read_csv(dataset["metadata.csv"])
# Access images
image_paths = metadata["image_path"].tolist()
License
CC-BY-4.0
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