Agent Skills
› aiming-lab/AutoResearchClaw
› cv-classification
cv-classification
GitHub提供图像分类任务的架构选择、训练配方及基准测试最佳实践，适用于CIFAR、ImageNet等场景。
Trigger Scenarios
进行图像分类模型选型
配置深度学习训练超参数
参考CIFAR或ImageNet基准性能
Install
npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -g -y
SKILL.md
Frontmatter
{
"name": "cv-classification",
"metadata": {
"author": "researchclaw",
"version": "1.0",
"category": "domain",
"priority": "3",
"references": "He et al., Deep Residual Learning, CVPR 2016; Dosovitskiy et al., An Image is Worth 16x16 Words, ICLR 2021",
"trigger-keywords": "classification,image,cifar,imagenet,resnet,vision,cnn,vit",
"applicable-stages": "9,10"
},
"description": "Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks."
}
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)
Version History
- e2e23c9 Current 2026-07-25 07:48
