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