cv-classification

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 16x16 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)