# Basics

- [Before you begin](/axelera/basics/before-you-begin.md): Before exploring the guides and running examples, review this page to ensure you're set up for success. It covers how to install DeGirum's PySDK and DeGirum Tools with Axelera devices.
- [First inference](/axelera/basics/first-inference.md): Run your first inference using copy-paste-ready code.
- [Specifying a model](/axelera/basics/specifying-a-model.md): Before diving into code, read this page to understand how PySDK represents and handles models.
- [Discover Axelera models](/axelera/basics/specifying-a-model/discover-axelera-models.md): Start with precompiled Axelera models that run out of the box—learn how to pick the right variant for your device.
- [Inference setup](/axelera/basics/specifying-a-model/inference-setup.md): PySDK gives you flexibility in where models are stored and where inferences run. This page walks through common setups (cloud, local, and hybrid) so you can choose what fits your workflow.
- [Model properties](/axelera/basics/specifying-a-model/model-properties.md): See what you can tune on your model—and why it matters. This page introduces the model\_properties field and shows how to inspect, change, and group model settings by task.
- [Running inference](/axelera/basics/running-inference.md): Learn how to run inference with your model using simple, flexible methods—single images, video streams, or entire folders.
- [Images](/axelera/basics/running-inference/images.md): Run inference on a single image using a URL, file path, or NumPy array. This page shows how to use each input type with a loaded model.
- [Videos](/axelera/basics/running-inference/videos.md): Learn how to run real-time inference on video streams using predict\_stream. This page covers video files, webcams, and RTSP sources—all with minimal setup.
- [Folders](/axelera/basics/running-inference/folders.md): Run inference on entire image folders with predict\_dir, streaming results efficiently from flat or nested directories.
- [Inference results](/axelera/basics/inference-results.md): Understand the structure and purpose of the InferenceResults object returned by model inference. Learn how each field supports visualization, inspection, saving, or real-time streaming.
- [Inspecting results](/axelera/basics/inference-results/inspecting-results.md): Understand the structure of PySDK inference results so you can inspect labels, scores, and metadata before visualizing, saving, or streaming them.
- [Visualizing results](/axelera/basics/inference-results/visualizing-results.md): Learn how to view original frames, model-ready tensors, and overlay images returned in each inference result.
- [Saving results](/axelera/basics/inference-results/saving-results.md): Capture inference outputs as structured data or images so you can reuse them in downstream tools, dashboards, or datasets.
- [Streaming results](/axelera/basics/inference-results/streaming-results.md): Stream inference outputs in real time to displays, message buses, or remote services using PySDK result objects.
- [Measuring performance](/axelera/basics/measuring-performance.md): Measure latency and throughput for DeGirum models, capture per-stage timings, and apply repeatable test loops backed by consistent validation.