ResNet on CIFAR-10
PythonPyTorchConvolutional Neural NetworksImage ClassificationData AugmentationPaper ReadingComputer VisionDeep Learning
The problem
Understanding residual networks means testing how architecture and augmentation affect image classification.
How I solved it
Implemented ResNet architecture for image classification on CIFAR-10. Analyzed different architectures and the impact of data augmentation on model performance.
- Implemented ResNet architecture for CIFAR-10
- Analyzed various model architectures
- Studied data augmentation impact
- Optimized model performance
The results
- reported test accuracy
- 87.92%
- gain across configurations
- ~12.92 pp
Reported 87.92% test accuracy on 10-class CIFAR-10, approximately 12.92 percentage points above the earlier ~75% configuration. Delivered training, evaluation, checkpoint and resume workflows.
Evaluation context. Both augmentation and residual-block count changed. This is a configuration comparison, not an isolated augmentation ablation or independently rerun benchmark.