Datasets:

eidolon08
/

yolo_seg_labeled_data_for_small_toycar

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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YOLO Segmentation Labeled Data for Small Toy Car

This is an autonomous driving YOLO object detection dataset for small RC/toy cars. It contains bounding box annotations on images captured in parking and track driving environments.

(Parking_Rear)	(Parking_Front)
	(Mission)
	(Crosswalk)

Dataset Structure

The dataset consists of a total of 4 subsets and approximately 75,000 images. Each subset includes train / valid / test splits and is provided as a tar.gz archive.

Subset	Description	Number of Images	Number of Classes
`parking_front`	Front parking environment	10,719	3
`parking_rear`	Rear parking environment	5,639	3
`track`	Track driving environment	20,135	4
`track_crosswalk`	Track crosswalk environment	38,692	1

Class Information

parking_front

ID	Class Name	Description
0	`out_line`	Parking zone outline
1	`parking_lot`	Parking lot area
2	`parking_space`	Individual parking space

parking_rear

ID	Class Name	Description
0	`end_line`	Parking end line
1	`parking_lot`	Parking lot area
2	`parking_space`	Individual parking space

track

ID	Class Name	Description
0	`car`	Car
1	`lane1`	Lane 1
2	`lane2`	Lane 2
3	`traffic_light`	Traffic light

track_crosswalk

ID	Class Name	Description
0	`crosswalk`	Crosswalk

How to Use

1. Download and Extract

# Install huggingface_hub
pip install huggingface_hub

# Download the desired subset (e.g., track)
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="eidolon08/yolo_seg_labeled_data_for_small_toycar",
    filename="track.tar.gz",
    repo_type="dataset"
)

Or via CLI:

Bash
huggingface-cli download eidolon08/yolo_seg_labeled_data_for_small_toycar \
    track.tar.gz --repo-type dataset --local-dir ./data
Extract the archive:

Bash
tar -xzf track.tar.gz
2. Directory Structure
The structure of each subset after extraction is as follows:

{subset}/
├── data.yaml
├── train/
│   ├── images/
│   │   ├── chunk_0000/   # Image files (in chunks of 1,000)
│   │   ├── chunk_0001/
│   │   └── ...
│   └── labels/
│       ├── chunk_0000/   # YOLO format annotations (.txt)
│       ├── chunk_0001/
│       └── ...
├── valid/
│   ├── images/
│   └── labels/
└── test/
    ├── images/
    └── labels/
3. YOLO Training
Modify the paths in data.yaml to fit your local environment and start training:

YAML
train: ./track/train/images
val: ./track/valid/images
test: ./track/test/images

nc: 4
names: ['car', 'lane1', 'lane2', 'traffic_light']
Bash
yolo detect train data=data.yaml model=yolov8n.pt epochs=100
Annotation Format
The dataset uses the YOLO format (.txt). Each line represents one object:

<class_id> <x_center> <y_center> <width> <height>
All values are normalized (0~1) relative to the image dimensions.

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Total file size:

5.85 GB