Datasets:
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The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1811, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
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/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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3 0.138520 0.903640 0.117880 0.019270 |
1 0.497000 0.517780 0.884370 0.757290 |
3 0.171890 0.920580 0.075670 0.023800 |
1 0.509040 0.528180 0.912840 0.787320 |
3 0.827050 0.942370 0.127670 0.028430 |
1 0.513070 0.484460 0.921680 0.854660 |
3 0.840420 0.928860 0.151610 0.025730 |
1 0.477910 0.499840 0.826090 0.799820 |
3 0.816430 0.923050 0.063860 0.030990 |
1 0.524930 0.487710 0.861620 0.812630 |
3 0.193320 0.915860 0.061940 0.027510 |
1 0.481620 0.506110 0.794640 0.796800 |
3 0.829980 0.923780 0.055210 0.018590 |
1 0.488760 0.500300 0.821150 0.818360 |
3 0.816570 0.929920 0.055120 0.024350 |
1 0.523320 0.504550 0.796250 0.786540 |
3 0.187410 0.922500 0.046600 0.020600 |
1 0.456950 0.556740 0.801140 0.702860 |
3 0.124010 0.926530 0.040920 0.018400 |
1 0.526300 0.503590 0.823390 0.807970 |
3 0.870450 0.926120 0.039000 0.019270 |
1 0.381090 0.522870 0.631080 0.802610 |
3 0.132800 0.947310 0.047650 0.022250 |
1 0.344240 0.499380 0.605400 0.809800 |
3 0.134860 0.928910 0.058460 0.021520 |
1 0.355730 0.491370 0.639960 0.831720 |
3 0.151160 0.923280 0.067480 0.028290 |
1 0.508350 0.500430 0.811220 0.784710 |
3 0.813960 0.918970 0.053700 0.020550 |
1 0.491000 0.500340 0.841200 0.783290 |
3 0.182830 0.918880 0.074660 0.020420 |
1 0.500390 0.491100 0.875760 0.790200 |
3 0.182380 0.916040 0.076910 0.028750 |
1 0.522870 0.442620 0.810760 0.680290 |
3 0.840280 0.917010 0.073840 0.017490 |
1 0.495900 0.493750 0.889770 0.796610 |
3 0.189520 0.922550 0.102270 0.025500 |
0 0.582060 0.090270 0.548000 0.027600 |
1 0.509730 0.494440 0.755230 0.763650 |
0 0.361550 0.091100 0.430120 0.022980 |
1 0.495080 0.501170 0.757380 0.769700 |
0 0.544980 0.089910 0.598400 0.021610 |
1 0.511380 0.498920 0.762920 0.770340 |
0 0.361410 0.082810 0.425130 0.028470 |
1 0.482760 0.494710 0.773500 0.776420 |
1 0.489720 0.322550 0.767770 0.411900 |
3 0.161780 0.895630 0.031910 0.019410 |
1 0.478780 0.505970 0.756880 0.769280 |
3 0.156830 0.905150 0.030760 0.014650 |
1 0.520120 0.340810 0.792630 0.444770 |
3 0.839140 0.915080 0.028840 0.014880 |
1 0.498240 0.495720 0.837080 0.780360 |
3 0.165580 0.897600 0.037540 0.018860 |
1 0.520620 0.507900 0.797440 0.765580 |
3 0.838770 0.910640 0.046330 0.017580 |
1 0.463310 0.533030 0.863540 0.724610 |
3 0.127860 0.921860 0.057130 0.022890 |
1 0.462850 0.437400 0.804720 0.610530 |
3 0.131060 0.924010 0.037170 0.018910 |
1 0.560220 0.521130 0.783290 0.729410 |
3 0.891420 0.914310 0.048480 0.020230 |
1 0.549510 0.210670 0.828660 0.172350 |
3 0.873290 0.929690 0.064320 0.026000 |
1 0.532520 0.536050 0.871920 0.735180 |
3 0.855390 0.930510 0.065050 0.028750 |
1 0.509450 0.505010 0.834880 0.826410 |
3 0.197120 0.945660 0.087480 0.025820 |
1 0.490180 0.506160 0.816850 0.821100 |
3 0.797160 0.942870 0.085920 0.020100 |
1 0.519250 0.495490 0.871090 0.827150 |
3 0.817760 0.937330 0.085790 0.020420 |
1 0.502770 0.494710 0.830400 0.823030 |
3 0.820280 0.929870 0.080290 0.018450 |
1 0.514400 0.503550 0.861020 0.828890 |
3 0.215930 0.942140 0.084140 0.018130 |
1 0.493660 0.498050 0.811220 0.804670 |
3 0.835930 0.921860 0.039050 0.016570 |
1 0.492520 0.494990 0.792860 0.802930 |
3 0.158850 0.918200 0.051360 0.020230 |
1 0.491140 0.506610 0.826090 0.806230 |
3 0.161680 0.928540 0.055850 0.019360 |
1 0.507900 0.501260 0.835070 0.813780 |
3 0.183430 0.924420 0.054290 0.021420 |
1 0.505560 0.520710 0.811810 0.757660 |
3 0.845180 0.922040 0.052000 0.021380 |
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3 0.863950 0.933810 0.029800 0.016110 |
1 0.499240 0.504550 0.831820 0.813280 |
3 0.846740 0.924700 0.040240 0.017720 |
0 0.114080 0.145390 0.084820 0.020140 |
1 0.467520 0.538700 0.800270 0.762550 |
3 0.123830 0.941080 0.042390 0.016940 |
1 0.461570 0.510550 0.812410 0.799630 |
3 0.125980 0.929370 0.046140 0.013690 |
1 0.511240 0.498330 0.801510 0.758710 |
3 0.856490 0.903460 0.032910 0.019460 |
1 0.506800 0.500570 0.801370 0.763150 |
3 0.840560 0.907260 0.043400 0.016710 |
End of preview.
TDLA Training Dataset
YOLO-format object-detection dataset for Tibetan Document Layout Analysis (TDLA). The dataset contains bounding-box annotations for four layout classes found in Tibetan document page images and is split into training and validation sets using iterative multi-label stratification.
Overview
| Property | Value |
|---|---|
| Total images | 5588 |
| Total annotations | 13826 |
| Number of classes | 4 |
| Image format | JPEG (.jpg) |
| Label format | YOLO (.txt) |
| Split ratio | 80% train / 20% val |
| Stratification | Iterative multi-label stratification |
| Random seed | 42 |
Image Source
All images in this dataset are sourced from the Buddhist Digital Resource Center (BDRC) digital library.
Classes
| ID | Name | Images | % of dataset |
|---|---|---|---|
| 0 | header | 4280 | 76.6% |
| 1 | Text area | 5532 | 99.0% |
| 2 | footnote | 374 | 6.7% |
| 3 | footer | 3640 | 65.1% |
Annotation Process
Annotations were created on the Ultralytics HUB platform using the following two-stage workflow:
- Annotation -- Annotators drew bounding boxes for each of the four layout classes (header, Text area, footnote, footer) on every page image.
- Quality Control -- A dedicated reviewer inspected each annotated image, verifying label correctness, box tightness, and class assignment before the annotation was accepted into the dataset.
Split Methodology
The dataset was split into 80% training / 20% validation using iterative multi-label stratification (seed = 42). This approach treats each image as a multi-label sample (an image may contain several classes simultaneously) and iteratively assigns images to splits so that per-class proportions stay as close to the target ratio as possible. The result is a near-uniform 80/20 distribution for every class, as shown in the tables below.
Split Statistics
| Split | Images | % of total |
|---|---|---|
| train | 4470 | 80.0% |
| val | 1118 | 20.0% |
Class Distribution per Split
| Class | train | val | Total |
|---|---|---|---|
| header | 3424 (80.0%) | 856 (20.0%) | 4280 |
| Text area | 4425 (80.0%) | 1107 (20.0%) | 5532 |
| footnote | 299 (79.9%) | 75 (20.1%) | 374 |
| footer | 2912 (80.0%) | 728 (20.0%) | 3640 |
Directory Structure
TDLA_Training_dataset/
├── images/
│ ├── train/
│ └── val/
├── labels/
│ ├── train/
│ └── val/
├── train.txt
├── val.txt
├── data.yaml
└── README.md
Usage
Point your YOLO training config to data.yaml in this directory:
yolo detect train data=TDLA_Training_dataset/data.yaml
The train.txt and val.txt files list relative image paths for each split.
Label Format
Each .txt label file uses the standard YOLO format — one row per bounding box:
<class_id> <x_center> <y_center> <width> <height>
All coordinates are normalized to [0, 1] relative to image dimensions.
License
This dataset is released under the CC0 1.0 Universal (Public Domain Dedication). You are free to copy, modify, and distribute the data, even for commercial purposes, without asking permission.
Acknowledgements
This dataset was developed by Dharmaduta from specifications provided by the Buddhist Digital Resource Center (BDRC) for the BDRC Etext Corpus, with funding from the Khyentse Foundation.
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