DatasetLint¶

DatasetLint validates local robotics and physical AI datasets before they enter training, evaluation, or analysis pipelines.

It is intentionally small: local files in, deterministic report out. Use it to catch schema drift, broken references, timestamp problems, label issues, calibration mistakes, and adapter ingestion failures while the dataset is still cheap to fix.

First Five Minutes¶

git clone https://github.com/gagandeepreehal/datasetlint.git
cd datasetlint
python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e ".[dev,docs]"

datasetlint --version
datasetlint examples/minimal_dataset
datasetlint examples/bad_dataset --format json

examples/minimal_dataset should pass. examples/bad_dataset should fail and show the issue fields you will see in real projects.

Choose Your Path¶

Goal	Read This	First Command
Install and run the examples	Getting Started	datasetlint examples/minimal_dataset
Validate the native CSV/JSON folder format	Dataset Format	datasetlint lint DATASET_PATH
Tune checks or severities	Configuration	datasetlint DATASET_PATH --config DATASET_PATH/datasetlint.yaml
Inspect MCAP, ROS bag, Argoverse 2, LeRobot, COCO, KITTI, or other formats	Adapters	datasetlint inspect DATASET_PATH --adapter auto
Save JSON, Markdown, or HTML artifacts	Reports	datasetlint report DATASET_PATH --out datasetlint-report.json
Fix a failing run	Troubleshooting	datasetlint DATASET_PATH --format json
Add a CI gate	CI Templates	datasetlint DATASET_PATH --fail-on warning
Call DatasetLint from Python	Python API	from datasetlint import lint_dataset
Work on DatasetLint itself	Development	make check

What You Get¶

• Python 3.10+
• local files in, report out
• CLI and importable Python API
• no ROS, simulator, GPU, cloud, or model dependency
• native support for the folder CSV/JSON dataset format
• adapter manifests for common robotics and vision dataset formats
• JSON output and stable exit codes for CI

Documentation Map¶

Section	Best For
Start	New users installing from source and running the examples
Use DatasetLint	Dataset owners configuring checks, reports, stats, and diffs
Integrate	CLI users, Python callers, CI maintainers, and adapter users
Maintain	Contributors changing rules, adapters, docs, or release process

Common Outputs¶

Need	Command
Human-readable terminal report	datasetlint DATASET_PATH
JSON for automation	datasetlint DATASET_PATH --format json
Markdown report	datasetlint DATASET_PATH --format markdown
Static HTML report	datasetlint DATASET_PATH --format html > report.html
File artifact with inferred format	datasetlint report DATASET_PATH --out report.json
Dataset distribution summary	datasetlint stats DATASET_PATH
Regression check between folders	datasetlint diff OLD_DATASET NEW_DATASET --fail-on-regression

Exit Codes¶

Code	Meaning
0	Command completed and did not meet the configured failure threshold
1	Validation failed, adapter validation failed, or diff regressions were found
2	Invalid command, unknown check or adapter, or invalid config

Current Status¶

DatasetLint v0.1 supports deep rule validation for its native folder format and normalized adapter manifests for generic folders, COCO, KITTI, Argoverse 2, LeRobot, nuScenes, Waymo, ROS bag, MCAP, Hugging Face, and plugin-provided datasets.

Waymo, ROS bag, MCAP, and Hugging Face default to lightweight index/cache manifests. Install the matching extra and use --deep when you need parser-backed metadata or sampled Hugging Face row diagnostics.