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User GuidesPython SDK Referencepicsellia
User Guides
Python SDK Reference
User GuidesRecipesPython SDK ReferenceDiscussions

Introduction

  • 🚀 Welcome to Picsellia's documentation
  • 💡 Why Picsellia ?
  • 💬 Glossary

DOCUMENTATION

  • 📑 General Information & Administration
    • Access Picsellia
    • Homepage
    • Setup your profile
    • Python SDK installation
    • Picsellia Organizations
    • Picsellia platform structure
    • Track operations with Jobs
    • Track your plan and usage
    • Users Management System
  • 📚 Data Management
    • Datalake - Philosophy and infrastructure
    • Datalake - Upload Data from local drive
    • Datalake - Import Data from your Cloud-based Object Storage
    • Datalake - Overview
    • Datalake - Tagging system
    • Datalake - Setup your own Metadata
    • Datalake - Projections
    • Datalake - Explore your Data with Embeddings
    • Datalake - Query Language
    • Datalake - Processings
    • Datalake - Dataset creation
    • Datalake - Summary & Workflow
    • Dataset - Dataset versioning system
    • Dataset - Assets overview
    • Dataset - Tagging system
    • Dataset - Set up the dataset
    • Dataset - Annotation import and export
    • Dataset - Annotation studio
      • Dataset - Annotation tool
      • Dataset - Annotation Campaign
    • Dataset - Processings
    • Dataset - Query language
    • DatasetVersion - Explore your Assets and Shapes with Embeddings
    • Dataset - Fork a Dataset Version
    • Dataset - Custom Shapes import from another version
  • 🔬 Data Science
    • Project - Creation & collaboration
    • Experiment - Creation
    • Experiment - Experiment overview
    • Experiment - Launch training
    • Experiment - Experiment Tracking
    • Experiment - Evaluation
    • Experiment - Comparison
    • Experiment - Export as a model
  • 🚀 Model Operations
    • Registry - Model versionning system
    • Registry - ModelVersion
    • Registry - Create a ModelVersion manually
    • Registry - Processings
    • Registry - Deploy a ModelVersion
    • Deployments - List of deployments
    • Deployments - Overview
    • Deployments - Dashboard metrics
    • Deployments - Configure and use a Pipeline
    • Deployments - Setup Alerts
    • Deployments - Inferences
    • Deployments - Predictions overview
    • Deployments - Prediction Review tool
      • Deployments - Prediction Review Campaign
      • Deployments - Prediction Review Tool
    • Deployments - Shadow deployment

Tutorials

  • ⏭️ Quick Start Guide
    • 1. Set up an account, an Organization and the Python SDK
    • 2. Access to the documentation
    • 3. What do you want to achieve with Picsellia ?
    • 4. Import your Data in the Datalake
    • 5. Create your first Dataset
    • 6. Import your Annotations
    • 7. Inherit a new DatasetVersion
    • 8. Create your Project and launch Experiments
    • 9. Deploy a ModelVersion and set the Data Pipeline up
    • 10. Make predictions
    • 11. Conclusion
  • 🏁 Community Edition - Start using Picsellia !
    • 1. Join Picsellia Community
    • 2. Dashboard overview
    • 3. Import your Data in the Datalake
    • 4. Create your first Dataset
    • 5. Create Annotations
    • 6. Inherit a new DatasetVersion
    • 7. Conclusion
  • 📦 Migrate your models to Picsellia
    • 1. Overview
    • 2. Create your first Model
    • 3. Make your ModelVersion Deployable
    • 4. Make your ModelVersion Trainable
    • 5. What's next?
  • 🧩 Integrate Picsellia into your training scripts
    • 1. Overview
    • 2. Initializing Picsellia connection & retrieve the Experiment
    • 3. Retrieve Data, files and parameters from Picsellia
    • 4. Track your trainings with Callbacks
    • 5. Test your ModelVersion with Picsellia Evaluation Interface
    • 6. Store new files
    • Final script
  • 🔎 Evaluate your Model Performances
    • 1. What's an Evaluation on Picsellia
    • 2. Evaluate a Classification Model
    • 3. Evaluate a Detection Model
    • 4. Evaluate a Segmentation Model
  • 📈 Monitoring an External Model
    • 1. Pre-requisite
    • 2. Classification - Monitor Model Predictions
    • 3. Object Detection - Monitor Model Predictions
    • 4. Segmentation - Monitor Model Predictions
  • 🤖 Create your own Processing
    • 1. Create a Private Processing
    • 2. Write the Processing script
    • 3. Configuration reference
  • 🐠 Configure Picsellia webhooks
    • 1. What's a Webhook?
    • 2. Events
    • 3. How to configure Webhooks on Picsellia
  • ☁️ Connect Picsellia to your own Cloud storage
    • 1. Configure your bucket
    • 1.1. AWS - Create a delegated Access for Picsellia
    • 1.2. GCS - Create access credentials
    • 1.3. Azure - Register a client application
    • 2. Datalake mechanism
    • 3. Configure your new Datalake
  • 🔄 Use MLFlow with Picsellia

In depth explanation

  • Custom Objects
    • Namespace
  • Computed Metrics
    • AE Outlier
    • KS Drift (Kolmogorov-Smirnov Test)
    • Tide Errors (Object detection)
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⏭️ Quick Start Guide

The perfect guide to quickly & easily create your first complete Computer Vision project with Picsellia and get the hang of the platform!

Updated 12 months ago


Deployments - Shadow deployment
1. Set up an account, an Organization and the Python SDK