# Developer Documentation

## Developer Documentation

- [EyePop.ai Introduction](https://docs.eyepop.ai/developer-documentation/eyepop.ai-introduction.md): A Self-Service AI Platform for Computer Vision Apps
- [Getting Started](https://docs.eyepop.ai/developer-documentation/getting-started.md): EyePop.ai enables developers to create Pops. Pops are vision workflows that process images, videos, and livestreams into structured AI predictions: bounding boxes, confidence scores, and classificatio
- [Pop Quick Start](https://docs.eyepop.ai/developer-documentation/getting-started/pop-quick-start.md): Create an endpoint and connect to it with an SDK.
- [Low Code Examples](https://docs.eyepop.ai/developer-documentation/getting-started/low-code-examples.md): A collection of App Pops to try for yourself
- [API Key](https://docs.eyepop.ai/developer-documentation/api-key.md): Creating and managing tokens for your Pop
- [Claude Code Skill](https://docs.eyepop.ai/developer-documentation/claude-code-skill.md)
- [Abilities](https://docs.eyepop.ai/developer-documentation/eyepop.ai-visual-intelligence/abilities.md)
- [Pretrained Models & Abilities](https://docs.eyepop.ai/developer-documentation/sdks/pretrained-models-and-abilities.md)
- [React/Node SDK](https://docs.eyepop.ai/developer-documentation/sdks/node-sdk.md): Simplify integrating AI on your Node server, React Web app, or React Native App
- [Render 2D (Visualization)](https://docs.eyepop.ai/developer-documentation/sdks/node-sdk/render-2d-visualization.md): The 2d visualization module for the Node SDK
- [Python SDK](https://docs.eyepop.ai/developer-documentation/sdks/python-sdk.md): Simplify integrating AI on your Python App
- [Composable Pops](https://docs.eyepop.ai/developer-documentation/sdks/python-sdk/composable-pops.md)
- [Dataset SDK (Node)](https://docs.eyepop.ai/developer-documentation/self-service-training/dataset-sdk-node.md)
- [How To Train a Model](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model.md): Overview
- [Defining Your Computer Vision Model](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/defining-your-computer-vision-model.md)
- [Example Use Case: Detecting Eyeglasses](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/example-use-case-detecting-eyeglasses.md): This example walks you through creating and training a model that identifies whether eyeglasses appear in an advertising image.
- [Preparing & Uploading Data](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/preparing-and-uploading-data.md)
- [Using EyePop.ai’s AutoLabeler](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/using-eyepop.ais-autolabeler.md)
- [Human Review](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/human-review.md)
- [Data Augmentation Setup](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/data-augmentation-setup.md)
- [Training in Progress](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/training-in-progress.md)
- [Deployment](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/deployment.md)
- [Previewing Results](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/previewing-results.md)
- [Iterative Training](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/iterative-training.md)
- [Deep Dives (FAQ)](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/deep-dives-faq.md)
- [EyePop On-Premise AI Runtime](https://docs.eyepop.ai/developer-documentation/deployment/eyepop-on-premise-ai-runtime.md): Deployable, Flexible, API-First Inference Engine
- [On Premise IP Camera analysis](https://docs.eyepop.ai/developer-documentation/deployment/eyepop-on-premise-ai-runtime/on-premise-ip-camera-analysis.md): Developer Architecture Overview: Camera Input to Cloud Inference with On-Prem App
- [NVIDIA Jetson Orin NX](https://docs.eyepop.ai/developer-documentation/deployment/eyepop-on-premise-ai-runtime/nvidia-jetson-orin-nx.md): Developer Preview of deploying the EyePop.ai runtime to the Jetson Orin NX
- [Qualcomm Dragonwing (QNN)](https://docs.eyepop.ai/developer-documentation/deployment/eyepop-on-premise-ai-runtime/qualcomm-dragonwing-qnn.md): Developer Preview of deploying the EyePop.ai runtime to Qualcomm Dragonwing (QNN) devices
- [Connecting EyePop.ai Cloud AI to Video Behind a Firewall](https://docs.eyepop.ai/developer-documentation/deployment/connecting-eyepop.ai-cloud-ai-to-video-behind-a-firewall.md)
- [Pop](https://docs.eyepop.ai/developer-documentation/glossary/pop.md)
- [Compute Unit](https://docs.eyepop.ai/developer-documentation/glossary/compute-unit.md)
- [Permanent Session](https://docs.eyepop.ai/developer-documentation/glossary/permanent-session.md)