This content originally appeared on DEV Community and was authored by Amirali Soltani rad
Introduction
Reaching high accuracy on standard benchmarks like CIFAR-100 is challenging. In this article, I show how I trained a ResNet-50 with PyTorch to achieve 84.35% test accuracy and provide a production-ready repository with a live Streamlit demo.
GitHub & Demo: cifar100-classification
Why CIFAR-100 & ResNet-50?
- CIFAR-100: 100 fine-grained categories, only 500 images per class → hard to classify.
- ResNet-50: classic, widely adopted, and great for transfer learning.
- Public ResNet-50 implementations rarely exceed ~81% on CIFAR-100; hitting 84.35% is rare and significant.
Challenges
- High class similarity → tough separation
- Limited images → risk of overfitting
- Mid-range GPU constraints → careful optimization needed
Model & Training Strategy
Architecture: Pretrained ResNet-50 (ImageNet), modified final layer for 100 classes
Training:
- OneCycleLR scheduling for fast convergence
- Progressive fine-tuning: FC only → Layers 3&4 + FC → Full model
- Mixed-precision training & checkpointing
Data Augmentation:
- Resize → ColorJitter → RandomHorizontalFlip
- RandomRotation (±15°) → RandomAffine → GaussianBlur → RandomErasing
- Mixup α=0.8 & CutMix α=0.8 (p=0.5)
- Label smoothing
Results
| Metric | Value |
|---|---|
| Test Accuracy | 84.35% |
| Test Loss | 0.7622 |
| Best Val Loss | 0.9729 |
| Epochs | 66 |
Even on mid-range GPUs like NVIDIA GTX 1650 (~15.5 hours), this setup achieved state-of-the-art ResNet-50 results on CIFAR-100.
Interactive Demo & Repo
Streamlit app lets you upload images and get predictions + confidence scores.
Repo structure:
cifar100-classification/
├── app.py
├── train.ipynb
├── requirements.txt
├── README.md
├── model_checkpoint.pth
└── ...
Why It Matters
- Classic architectures + modern tricks (augmentation, LR scheduling) can outperform expectations
- Sets a practical benchmark for ResNet-50 on CIFAR-100
- Next steps: ResNet-101, EfficientNet, ViT, larger datasets, API or edge deployment
Call to Action
- Share with fellow ML & CV enthusiasts
- Modify augmentations or architecture and test
- Comment your results — can we push beyond 85%?
This content originally appeared on DEV Community and was authored by Amirali Soltani rad
Amirali Soltani rad | Sciencx (2025-11-19T15:51:52+00:00) How I Reached 84.35% on CIFAR-100 Using ResNet-50 (PyTorch Guide). Retrieved from https://www.scien.cx/2025/11/19/how-i-reached-84-35-on-cifar-100-using-resnet-50-pytorch-guide-2/
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