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ModelCubLocal-First MLOps for Computer Vision

Train, annotate, and deploy CV models on your infrastructure. Version datasets like code. Zero cloud costs.

Quick Start

bash
# Install
pip install modelcub

# Initialize project
modelcub project init my-project
cd my-project

# Import dataset
modelcub dataset add --source ./data --name v1

# Launch UI
modelcub ui

Why ModelCub?

The Problem

Cloud platforms lock you in: Roboflow charges $500-$8k/month. Your data lives on their servers. Switching is painful.

DIY is fragmented: Label Studio + Ultralytics + custom scripts = integration hell. No version control. Hard to reproduce.

Privacy is compromised: Medical imaging, defense projects, and proprietary data can't use cloud platforms.

The Solution

ModelCub gives you a professional, integrated platform that runs entirely on your infrastructure:

  • ✅ Complete workflow (import → annotate → train → deploy)
  • ✅ 100% local, 100% private
  • ✅ Git-like version control for datasets
  • ✅ Clean Python SDK + CLI + Web UI
  • ✅ Free and open source

Comparison

ModelCubRoboflowLabel Studio + Ultralytics
CostFree$500-8k/moFree
Runs Locally
Annotation
Training
Version ControlLimited
Integrated
Setup Time2 min5 min30+ min

Python SDK

python
from modelcub import Project, Dataset

# Initialize project
project = Project.init("my-project")

# Import dataset
dataset = Dataset.from_yolo("./data", name="v1")

# Get statistics
print(f"Images: {dataset.num_images}")
print(f"Classes: {dataset.classes}")

# Access splits
train_images = dataset.splits['train'].images
print(f"Training images: {len(train_images)}")

Use Cases

Medical Imaging

HIPAA-compliant, on-premise training. Your patient data never leaves your servers. Perfect for hospitals and research institutions.

Startups & Indie Developers

Save $96k/year in cloud costs. Use that budget to hire engineers instead of paying SaaS fees.

Research Labs

Reproducible experiments with full audit trails. Version datasets alongside code. Perfect for academic papers.

Defense & Government

Air-gapped deployments. Zero external dependencies. Complete control over your data and infrastructure.

Architecture

ModelCub is built on clean, layered architecture:

┌─────────────────────────────────┐
│    CLI  │  SDK  │  Web UI      │
├─────────────────────────────────┤
│      FastAPI Backend            │
├─────────────────────────────────┤
│      Core Services              │
├─────────────────────────────────┤
│   File System State             │
│  (.modelcub directory)          │
└─────────────────────────────────┘

Key Principles:

  • API-First: Everything is composable
  • Stateless: No hidden database, all state in files
  • Format-Agnostic: YOLO internally, import/export anything
  • Git-Friendly: Human-readable text files

Get Started

bash
# Install
pip install modelcub

# Verify
modelcub --version

# Create your first project
modelcub project init demo-project

Read the Installation Guide or jump to the Quick Start.


ModelCub • MIT License • GitHubDocumentation

Released under the MIT License.