3. What do you want to achieve with Picsellia ?

Objectives:

• Understand the platform philosophy
• Understand the platform structure
• Use the Quick Start Guide properly depending on your needs

1. Picsellia's philosophy

Picsellia's mission is to empower technical teams to upgrade their Computer Vision Models into Remarkable Computer Vision pipelines.

With Picsellia, AI teams can Structure, Build & Observe their CV pipelines.

• Structure: Increase AI team time spent on high-value tasks by +33% to unleash collective intelligence.
• Operate: Create & Deploy CV models 3X time faster, with scalable infrastructures and automation.
• Observe: Gain actionable insights in seconds with full ML observability & experiment traceability.

You can leverage all the features available on the Picsellia platform to achieve this goal.

2. Platform's structure

The platform is divided into 3 main parts:

• Data Management (upload Data, create Dataset)
• Data Science (train your Model and assess their quality)
• Model Operations (Deploy, monitor, and retrain your Model automatically)

3. Your needs

Depending on your needs, your goals, and the material you already have, there are different ways to start using Picsellia:

1. You have your data but no Model trained yet ⇒ Start here
2. You have a ready-to-be-used Model and associated training Data ⇒ Start here

4. Vocabulary

Before deepening dive into the documentation, here is a bit of vocabulary used on the Picsellia platform:

• Datalake Unique & shared place gathering all Data (images) related to an Organization
• Data An image & associated Metadata contained in the Datalake
• DataTag Additional Metadata that can be assigned to one or several Data in order to organize a Datalake
• Dataset A placeholder for multiple Datasetversion
• DatasetVersion A subset of Data inherited from the Datalake that will be annotated to be used later for a ModelVersion training
• Asset An image & associated Metadata contained in a DatasetVersion, each asset is linked to its Data from the Datalake.
• AssetTag Additional Metadata that can be assigned to one or several Asset in order to organize a DatasetVersion
• Label An object that will store a class name and an id.
• Annotation A set of Shapes annotated by one person.
• Shape An annotated object can be a classification, a rectangle, a polygon, a line, or a point.