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Cv classification

Skill thada2402/AutoResearchClaw/researchclaw/skills/builtin/domain/cv-classification

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

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npx -y skills add thada2402/AutoResearchClaw --skill cv-classification

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SKILL.md

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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)

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Gives 0 of the 12 instructions most hr recruiting skills give in 265 tokens

Counted across 356 of the 357 authors here whose files we hold, read 2026-08-07

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  • use appropriate architectures per dataset scale
  • use AdamW or SGD with specified learning rates
  • apply weight decay between 0.01 and 0.1 for AdamW
  • use 5e-4 weight decay for SGD
  • apply RandomCrop, RandomHorizontalFlip, and Cutout or CutMix
  • use 5-10 epochs of linear warmup for transformers

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