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Object Detection Dataset Scripts

5 scripts to convert, validate, inspect, diff, and sample object detection datasets on the Hub. Supports 6 bbox formats — no setup required. This repository is inspired by panlabel

Quick Start

Convert bounding box formats without cloning anything:

# Convert COCO-style bboxes to YOLO normalized format
uv run convert-hf-dataset.py merve/coco-dataset merve/coco-yolo \
    --from coco_xywh --to yolo --max-samples 100

That's it! The script will:

Load the dataset from the Hub
Convert all bounding boxes in-place
Push the result to a new dataset repo
View results at: https://huggingface.co/datasets/merve/coco-yolo

Scripts

Script	Description
`convert-hf-dataset.py`	Convert between 6 bbox formats and push to Hub
`validate-hf-dataset.py`	Check annotations for errors (invalid bboxes, duplicates, bounds)
`stats-hf-dataset.py`	Compute statistics (counts, label histogram, area, co-occurrence)
`diff-hf-datasets.py`	Compare two datasets semantically (IoU-based annotation matching)
`sample-hf-dataset.py`	Create subsets (random or stratified) and push to Hub

Supported Bbox Formats

All scripts support these 6 bounding box formats, matching the panlabel Rust CLI:

Format	Encoding	Coordinate Space
`coco_xywh`	`[x, y, width, height]`	Pixels
`xyxy`	`[xmin, ymin, xmax, ymax]`	Pixels
`voc`	`[xmin, ymin, xmax, ymax]`	Pixels (alias for `xyxy`)
`yolo`	`[center_x, center_y, width, height]`	Normalized 0–1
`tfod`	`[xmin, ymin, xmax, ymax]`	Normalized 0–1
`label_studio`	`[x, y, width, height]`	Percentage 0–100

Conversions go through XYXY pixel-space as the intermediate representation, so any format can be converted to any other format.

Common Options

All scripts accept flexible column mapping. Datasets can store annotations as flat columns or nested under an objects dict — both layouts are handled automatically.

Option	Description
`--bbox-column`	Column containing bboxes (default: `bbox`)
`--category-column`	Column containing category labels (default: `category`)
`--width-column`	Column for image width (default: `width`)
`--height-column`	Column for image height (default: `height`)
`--split`	Dataset split (default: `train`)
`--max-samples`	Limit number of samples (useful for testing)
`--hf-token`	HF API token (or set `HF_TOKEN` env var)
`--private`	Make output dataset private

Every script supports --help to see all available options:

uv run convert-hf-dataset.py --help

Convert (`convert-hf-dataset.py`)

Convert bounding boxes between any of the 6 supported formats:

# COCO -> XYXY
uv run convert-hf-dataset.py merve/license-plates merve/license-plates-voc \
    --from coco_xywh --to voc

# YOLO -> COCO
uv run convert-hf-dataset.py merve/license-plates merve/license-plates-yolo \
    --from coco_xywh --to yolo

# TFOD (normalized xyxy) -> COCO
uv run convert-hf-dataset.py merve/license-plates-tfod merve/license-plates-coco \
    --from tfod --to coco_xywh

# Label Studio (percentage xywh) -> XYXY
uv run convert-hf-dataset.py merve/ls-dataset merve/ls-xyxy \
    --from label_studio --to xyxy

# Test on 10 samples first
uv run convert-hf-dataset.py merve/dataset merve/converted \
    --from xyxy --to yolo --max-samples 10

# Shuffle before converting a subset
uv run convert-hf-dataset.py merve/dataset merve/converted \
    --from coco_xywh --to tfod --max-samples 500 --shuffle

Option	Description
`--from`	Source bbox format (required)
`--to`	Target bbox format (required)
`--batch-size`	Batch size for map (default: 1000)
`--create-pr`	Push as PR instead of direct commit
`--shuffle`	Shuffle dataset before processing
`--seed`	Random seed for shuffling (default: 42)

Validate (`validate-hf-dataset.py`)

Check annotations for common issues:

# Basic validation
uv run validate-hf-dataset.py merve/coco-dataset

# Validate YOLO-format dataset
uv run validate-hf-dataset.py merve/yolo-dataset --bbox-format yolo

# Validate TFOD-format dataset
uv run validate-hf-dataset.py merve/tfod-dataset --bbox-format tfod

# Strict mode (warnings become errors)
uv run validate-hf-dataset.py merve/dataset --strict

# JSON report
uv run validate-hf-dataset.py merve/dataset --report json

# Stream large datasets without full download
uv run validate-hf-dataset.py merve/huge-dataset --streaming --max-samples 5000

# Push validation report to Hub
uv run validate-hf-dataset.py merve/dataset --output-dataset merve/validation-report

Issue Codes:

Code	Level	Description
E001	Error	Bbox/category count mismatch
E002	Error	Invalid bbox (missing values)
E003	Error	Non-finite coordinates (NaN/Inf)
E004	Error	xmin > xmax
E005	Error	ymin > ymax
W001	Warning	No annotations in example
W002	Warning	Zero or negative area
W003	Warning	Bbox before image origin
W004	Warning	Bbox beyond image bounds
W005	Warning	Empty category label
W006	Warning	Duplicate file name

Stats (`stats-hf-dataset.py`)

Compute rich statistics for a dataset:

# Basic stats
uv run stats-hf-dataset.py merve/coco-dataset

# Top 20 label histogram, JSON output
uv run stats-hf-dataset.py merve/dataset --top 20 --report json

# Stats for TFOD-format dataset
uv run stats-hf-dataset.py merve/dataset --bbox-format tfod

# Stream large datasets
uv run stats-hf-dataset.py merve/huge-dataset --streaming --max-samples 10000

# Push stats report to Hub
uv run stats-hf-dataset.py merve/dataset --output-dataset merve/stats-report

Reports include: summary counts, label distribution, annotation density, bbox area/aspect ratio distributions, per-category area stats, category co-occurrence pairs, and image resolution distribution.

Diff (`diff-hf-datasets.py`)

Compare two datasets semantically using IoU-based annotation matching:

# Basic diff
uv run diff-hf-datasets.py merve/dataset-v1 merve/dataset-v2

# Stricter matching
uv run diff-hf-datasets.py merve/old merve/new --iou-threshold 0.7

# Per-annotation change details
uv run diff-hf-datasets.py merve/old merve/new --detail

# JSON report
uv run diff-hf-datasets.py merve/old merve/new --report json

Reports include: shared/unique images, shared/unique categories, matched/added/removed/modified annotations.

Sample (`sample-hf-dataset.py`)

Create random or stratified subsets:

# Random 500 samples
uv run sample-hf-dataset.py merve/dataset merve/subset -n 500

# 10% fraction
uv run sample-hf-dataset.py merve/dataset merve/subset --fraction 0.1

# Stratified sampling (preserves class distribution)
uv run sample-hf-dataset.py merve/dataset merve/subset \
    -n 200 --strategy stratified

# Filter by categories
uv run sample-hf-dataset.py merve/dataset merve/subset \
    -n 100 --categories "cat,dog,bird"

# Reproducible sampling
uv run sample-hf-dataset.py merve/dataset merve/subset \
    -n 500 --seed 42

Option	Description
`-n`	Number of samples to select
`--fraction`	Fraction of dataset (0.0–1.0)
`--strategy`	`random` (default) or `stratified`
`--categories`	Comma-separated list of categories to filter by
`--category-mode`	`images` (default) or `annotations`

Run Locally

# Clone and run
git clone https://huggingface.co/datasets/uv-scripts/panlabel
cd panlabel
uv run convert-hf-dataset.py input-dataset output-dataset --from coco_xywh --to yolo

# Or run directly from URL
uv run https://huggingface.co/datasets/uv-scripts/panlabel/raw/main/convert-hf-dataset.py \
    input-dataset output-dataset --from coco_xywh --to yolo

Works with any Hugging Face dataset containing object detection annotations — COCO, YOLO, VOC, TFOD, or Label Studio format.

Object Detection Dataset Scripts

Quick Start

Scripts

Supported Bbox Formats

Common Options

Convert (convert-hf-dataset.py)

Validate (validate-hf-dataset.py)

Stats (stats-hf-dataset.py)

Diff (diff-hf-datasets.py)