— name: cv-classification description: Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks. metadata: category: domain trigger-keywords: "classification,image,cifar,imagenet,resnet,vision,cnn,vit" applicable-stages: "9,10" priority: "3" version: "1.0" author: researchclaw references: "He et al., Deep Residual Learning, CVPR 2016; Dosovitskiy et al., An Image is Worth 16×16 Words, ICLR 2021" — ## 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)