# Cv Classification

> Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

- Skill: `enuno/cv-classification` (Agent Skill)
- Install (CLI): `npx skillmds@latest add enuno/cv-classification`
- Raw SKILL.md: https://api.skillmd.com/api/skills/enuno/cv-classification/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: enuno (https://skillmd.com/u/enuno)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/enuno/cv-classification

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## Image Classification Best Practice
Architecture selection:
- Small scale (CIFAR-10/100): ResNet-18/34, WideResNet, Simple ViT
- Medium scale: ResNet-50, EfficientNet-B0/B1, DeiT-Small
- Large scale: ViT-B/16, ConvNeXt, Swin Transformer

Training recipe:
- Optimizer: AdamW (lr=1e-3 to 3e-4) or SGD (lr=0.1 with cosine decay)
- Weight decay: 0.01-0.1 for AdamW, 5e-4 for SGD
- Data augmentation: RandomCrop, RandomHorizontalFlip, Cutout/CutMix
- Warmup: 5-10 epochs linear warmup for transformers
- Batch size: 128-256 for CNNs, 512-1024 for ViTs (if memory allows)

Standard benchmarks:
- CIFAR-10: ~96% (ResNet-18), ~97% (WideResNet)
- CIFAR-100: ~80% (ResNet-18), ~84% (WideResNet)
- ImageNet: ~76% (ResNet-50), ~81% (ViT-B/16)