# Your datasets deserve version control

Git for your computer vision data. Track changes, compare versions, and ensure every experiment is reproducible.

v3

## Current Version

- **+2,340 images**  
- **24 Datasets**  
- **100% Reproducibility**  
- **0 Data loss**  
- **5x Faster iterations**  
- **∞ Version history**

## Used by teams at

## Track every change, reproduce any result

Your datasets evolve constantly—new images, corrected labels, filtered samples. Without version control, you're flying blind.

### Immutable snapshots

Every dataset version is a permanent snapshot. Reference exact data states in experiments.

### Label management

Create, rename, and merge labels across your dataset. Keep your taxonomy clean and consistent.

### Fork for experiments

Fork dataset versions to test hypotheses without affecting production data.

### Audit-ready history

Complete changelog with who changed what, when, and why. Perfect for compliance.

### Version Timeline

Interactive  
- **v1.0**  
  *Jan 15*  
- **v1.1**  
  *Feb 02*  
- **v2.0**  
  *Feb 28*  
  *production*  
- **v2.1**  
  *Mar 15*

## Dataset: defect-detection

- **Last modified**: Feb 28
- **Images**: 2,100
- **Annotations**: 8,400
- **Change**: +650

## Everything you need to manage datasets

Version, organize, and share your datasets. Everything connects to your experiments.

### Git-like Version Control

Track every change to your datasets. Compare versions, rollback mistakes, and branch for experiments. Full lineage from raw data to trained models.

- **100% Reproducibility**  
- **5x Faster discovery**

### Smart Data Organization

Tag, filter, and slice your data in seconds. Create custom views, save queries, and share collections. No more hunting through folders.

- **60% Less coordination**

### Team Collaboration

Share datasets across teams with fine-grained permissions. Track who changed what, when, and why. Comments and reviews built-in.

- **100% Audit compliance**

### Full Data Lineage

Trace any prediction back to its training data. Audit-ready lineage for compliance. Understand model behavior through data.

## Programmatic dataset management

Full Python SDK with type hints, auto-completion, and comprehensive documentation. Integrate datasets directly into your ML pipelines.

### Example Code

```python
from picsellia import Client

client = Client()
datalake = client.get_datalake()

# Get or create dataset
dataset = client.get_dataset("defect-detection")

# Create a new version
version = dataset.create_version(
  version="v3",
  description="Added edge cases"
)

# Add data from datalake
data = datalake.list_data(
  tags=["edge-case", "validated"]
)
version.add_data(data)
```

### Dataset Browser

- **GridSplit**
    - **train**: 8,400 (70%)
    - **validation**: 1,800 (15%)
    - **test**: 1,800 (15%)
- **Total images**: 12K
- **Total annotations**: 48K
- **Balanced Class dist.**

## Structure your data the right way

Proper data splits are crucial for model performance. Create reproducible train/val/test splits, stratify by class, and ensure no data leakage.

- **Automatic stratified splits by class distribution**  
- **Custom split ratios with reproducible seeds**  
- **No overlap guarantee between splits**  
- **Re-split without losing annotations**

## Fits into your existing workflow

Datasets connect directly to annotations, experiments, and deployments. No manual handoffs.

## Ready to version your datasets?

Free trial, no credit card. Start versioning your datasets today.

- **No credit card required**  
- **14-day free trial**  
- **Unlimited versions**  
- **50M+ Images versioned**  
- **100% Reproducibility**  
- **0 Data loss**