# Cv Classification

Best practices for image classification tasks.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Media
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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)