🚀 Ultralytics Replicate

Deploy YOLO11n to Replicate at https://replicate.com/ultralytics/yolo11n with ready-to-use Cog configuration and automated CI/CD workflow.

🗂️ Repository Structure

This repository provides optimized Replicate deployment for the YOLO11n model with automated CI/CD workflow.

ultralytics/replicate/
│
├── yolo11n/                  # YOLO11n model deployment
│   ├── cog.yaml              # Cog configuration
│   ├── predict.py            # Prediction interface
│   └── README.md             # Model documentation
│
├── .github/workflows/        # Automated deployment
│   ├── push.yml              # Model deployment workflow
│   ├── ci.yml                # Code quality checks
│   └── format.yml            # Code formatting
│
├── test_prediction.py        # Local testing utility
├── requirements.txt          # Dependencies
├── LICENSE                   # AGPL-3.0 license
└── README.md                 # This file

⚡ Quick Start

Deploy YOLO11n Model

Model will deploy to https://replicate.com/ultralytics/yolo11n:

# Clone repository
git clone https://github.com/ultralytics/replicate.git
cd replicate

# Deploy to Replicate
cd yolo11n
cog login
cog push r8.im/ultralytics/yolo11n

Automated Deployment with GitHub Actions

1. Setup secrets:

   • Go to repository Settings → Secrets → Actions
   • Add REPLICATE_API_TOKEN with your Replicate API token

2. Deploy:

   • Manual: Actions tab → "Push YOLO11n to Replicate" → Run workflow
   • Automatic: Push changes to main branch auto-deploys

🛠️ Installation

Install Cog (Replicate's deployment tool):

sudo curl -o /usr/local/bin/cog -L https://github.com/replicate/cog/releases/latest/download/cog_$(uname -s)_$(uname -m)
sudo chmod +x /usr/local/bin/cog

For local development and testing:

pip install -r requirements.txt

🎯 YOLO11n Model

• Purpose: Official YOLO11n object detection
• Parameters: 2.6M parameters
• Classes: 80 COCO classes
• Performance: 39.5 mAP50-95 on COCO dataset
• Speed: Optimized for real-time inference

🔧 Model Setup

The model will be automatically downloaded by ultralytics when needed:

from ultralytics import YOLO

model = YOLO("yolo11n.pt")  # Downloads automatically if not present

🧪 Local Testing

Test the model locally before deploying:

# Test YOLO11n
python test_prediction.py --model yolo11n --image test.jpg

🚀 Features

• 🏎️ Optimized: PyTorch model for fast inference
• 🤖 Automated: GitHub Actions for CI/CD
• 📦 Ready-to-use: Pre-configured YOLO11n deployment
• 📊 Scalable: Auto-scaling Replicate infrastructure
• 🎯 Simple: Single model focus

💡 Contribute

Ultralytics thrives on community collaboration, and we deeply value your contributions! Whether it's reporting bugs, suggesting features, or submitting code changes, your involvement is crucial.

• Reporting Issues: Encounter a bug? Please report it on GitHub Issues.
• Feature Requests: Have an idea for improvement? Share it via GitHub Issues.
• Pull Requests: Want to contribute code? Please read our Contributing Guide first, then submit a Pull Request.
• Feedback: Share your thoughts and experiences by participating in our official Survey.

A heartfelt thank you 🙏 goes out to all our contributors! Your efforts help make Ultralytics tools better for everyone.

📄 License

Ultralytics offers two licensing options to accommodate diverse needs:

• AGPL-3.0 License: Ideal for students, researchers, and enthusiasts passionate about open collaboration and knowledge sharing. This OSI-approved open-source license promotes transparency and community involvement. See the LICENSE file for details.
• Enterprise License: Designed for commercial applications, this license permits the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the copyleft requirements of AGPL-3.0. For commercial use cases, please inquire about an Ultralytics Enterprise License.

📮 Contact

For bug reports or feature suggestions related to this project or other Ultralytics projects, please use GitHub Issues. For general questions, discussions, and community support, join our Discord server!