Fiftyone zoo

docs/getting_started/focused_getting_starteds/FiftyOne_Zoo/index.md

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+# Getting Started with the FiftyOne Zoo
+
+Welcome to the FiftyOne Zoo! This interactive guide will walk you through using, customizing, and extending the Zoo’s capabilities with datasets and models — all from within Jupyter Notebooks.
+
+## Who this is for
+
+This experience is designed for:
+- **Data scientists** looking to explore CV datasets quickly
+- **ML engineers** wanting to prototype with pre-trained models
+- **Computer vision practitioners** seeking to integrate custom models
+
+Whether you're exploring public data, testing models, or wrapping your own contributions, the Zoo helps you streamline your computer vision workflows.
+
+## Assumed Knowledge
+
+To get the most out of this series, you should have:
+- Basic knowledge of Python and Jupyter Notebooks
+- Familiarity with CV tasks (classification, detection, segmentation)
+- Some exposure to FiftyOne (optional but helpful)
+
+## Time to complete
+
+- **Step 1:** 20–30 minutes  
+- **Step 2:** 15–20 minutes  
+- **Step 3:** 30–45+ minutes (more if wrapping your own model)
+
+## Required packages
+
+Before getting started, make sure the following packages are installed:
+
+```bash
+pip install fiftyone torch torchvision
+```
+
+## Content
+
+### [Step 1: Exploring the FiftyOne Dataset Zoo](./step1.ipynb)  
+Get hands-on with the FiftyOne Dataset Zoo. You'll learn how to list, load, and explore built-in datasets as well as work with remotely-sourced datasets hosted on external URLs or GitHub repositories.
+
+### [Step 2: Using the FiftyOne Model Zoo](./step2.ipynb)  
+Apply pre-trained models to datasets using the built-in Zoo. You'll see how to use classification and detection models, visualize results, and iterate faster.
+
+### [Step 3: Using Remotely-Sourced Zoo Models](./step3.ipynb)  
+Learn how to integrate custom or community models hosted on GitHub or public URLs. This step shows how to load and run these models just like native zoo models — ideal for advanced use cases or team-specific models.