Picsellia CV Engine

Picsellia CV Engine is a modular engine for building, testing, and deploying computer vision pipelines — fully integrated with the Picsellia platform.

Whether you're transforming datasets, training models, or tracking experiments, this engine helps you organize everything into clean, reusable components.

🧠 What’s a pipeline?

A pipeline is a structured sequence of actions — like:

• 🧼 Preprocessing images
• 🧪 Training a model
• 📊 Evaluating predictions
• ☁️ Uploading results to Picsellia

Each action is implemented as a step — a small, focused function decorated with @step.

You can chain together these steps inside a @pipeline, and run it locally or on Picsellia.

🚀 Getting Started

Install from PyPI:

• With uv:

uv add picsellia-cv-engine
uv add picsellia-pipelines-cli

• With pip:

pip install picsellia-cv-engine
pip install picsellia-pipelines-cli

🛠 Create and run your first pipeline

Use the Picsellia Pipelines CLI to scaffold and manage your pipelines.

1. Initialize a pipeline

pxl-pipeline init my_pipeline --type training --template ultralytics

This generates everything you need: config, Dockerfile, code templates, and a virtual environment.

➡️ See pipeline lifecycle and commands

2. Run it locally

pxl-pipeline test my_pipeline

3. Deploy to Picsellia

pxl-pipeline deploy my_pipeline

🔎 Want real examples? Explore the pipeline usage templates for training and processing workflows.

📘 Documentation

The full documentation is available at: 👉 https://picselliahq.github.io/picsellia-cv-engine/

It includes:

• Getting Started
• CLI Usage Guide
• API Reference
• Pipeline templates & examples

🧑‍💻 Local Development

To contribute or explore the code:

1. Clone the repo

git clone https://github.com/picselliahq/picsellia-cv-engine.git
cd picsellia-cv-engine

2. Install dependencies

uv sync

3. Run the documentation

uv run mkdocs serve -a 127.0.0.1:8080

Then open http://127.0.0.1:8080 in your browser.