State-of-the-art CIFAR-100 classifier in PyTorch (ResNet-50, 84% accuracy) (github.com)

A new open-source PyTorch project delivers a production-ready CIFAR-100 image classifier (repo with training notebook, checkpoint and Streamlit demo) that reaches 84.35% test accuracy using transfer learning from ResNet-50 (ImageNet1K-V2) and a disciplined, three-stage progressive fine-tuning regimen. The pipeline pairs heavy data augmentation (ColorJitter, RandomErasing, RandomRotation, GaussianBlur, RandomPerspective) with Mixup/CutMix (α=0.8, switch p=0.5), label smoothing (0.1) and OneCycleLR with cosine annealing (30% warmup) to stabilize training; training uses SGD with Nesterov (momentum 0.9, weight_decay 5e-4), batch size 32, input 224×224 and a modified final FC layer (2048→100). Key implementation details: total parameters ≈23.7M, training loss uses SoftTargetCrossEntropy (validation uses CrossEntropy), early stopping (patience=10) halted training at 66/100 epochs, and the reported run took ≈15.5 hours on an NVIDIA GTX 1650. The fine-tuning schedule moves from FC-only to unfreezing deeper blocks with differential learning rates, then to full end-to-end tuning with layer-wise discriminative LRs—an approach that yields steady convergence (best val loss ~0.973) and strong generalization. For practitioners, this is a practical, reproducible baseline showing that careful augmentation + staged transfer learning can push CIFAR-100 performance without exotic architectures, and the included Streamlit app makes model inspection and demos trivial.