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Computer Vision Architecture Decision Tree

Picking a computer vision model shouldn’t be guesswork. This guide gives you a practical decision tree for selecting the right architecture across detection, segmentation, classification, and keypoint tasks based on accuracy, latency, and deployment constraints.

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From Use-Case to Model: A Practical Decision Tree

Learn how to move from your real-world application to the right vision architecture by following three simple steps:

• Choose your task
• Apply your constraints (accuracy, latency, edge, real-time)
• Select from strong, modern default architectures

Architecture Recommendations Across Vision Tasks

Explore model recommendations mapped by task and deployment constraint, including:

Object Detection (RT-DETRv2, YOLO11, DINOv3 + DETR)
Segmentation (Mask2Former, EoMT, MobileNetV4 + U-Net)
Classification & Zero-Shot (SigLIP 2, PE-Core, MobileCLIP2)
Keypoint Detection (ViTPose, RTMPose, YOLO-Pose)

Choosing the Right Model: Production Readiness Checklist

A comprehensive checklist to verify that your selected model is production-ready, covering:

Accuracy, latency, and memory fit
Licensing and open-source compliance
Framework compatibility (PyTorch, TensorFlow, JAX, etc.)
Export and deployment options (ONNX, TensorRT, Docker, cloud)
Maintenance status and documentation quality

Deep Dive: What Each Checklist Item Actually Means

Not all checklist items are equal - some are quick checks, others hide real complexity. This chapter walks through each property in detail, from spotting license gotchas between code and weights, to evaluating framework fit, deployment options like ONNX and TensorRT export, and the maintenance signals that separate actively supported models from abandoned ones.

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