Projects are a fundamental building block of the CrowdAI platform. Think of Projects as a sort of folder for collecting together all of the things you need to create and manage a single computer vision model.

There's a lot know know about Projects, so we've broken them down into a number of deep-dive articles where you can learn more about the specifics of each component. (We even have a self-guided tour of Projects, which you can ask our chat bot for!) I'll link to those at the bottom of this article, so think of this as a table of contents with a bit of an introduction.

What is a Project?

For our platform, a Project is a discrete unit of work that encompasses all of the things you need to create a new computer vision model or to update an existing one.

To do this, a Project associates all of the input data you need (media from your Datasets) and, based on a few settings you select when you create the Project, sets you up to quickly and easily create the training data you need to create or update a model.

Following the baking analogy I've used on so many articles: think of a Project as a bakery devoted to one specific baked good. You'll work with your suppliers (Datasets) to get your main ingredients, then prepare those ingredients (training data) and use them to bake something (train a model). Once your goods are baked, you can inspect them for quality (testing & evaluation), then put the best ones on display for your customers to enjoy (productionize a model).

Because a Project is devoted to one specific computer vision model, it's best to keep your Projects very well scoped and defined. Just like a human, the model that comes out of your Project will perform better if it's been trained to look for one or a few similar objects on similar media.

So don't create a "Detect Everything" Project: go instead for a "Bottle Cap Defect Detection" Project, for example. After all, you can create as many Projects as you want, so there's no need to force mismatched use-cases together.

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