CIFAR‑10 Image Classification

In this project, I worked with the CIFAR‑10 dataset to explore image classification. I began by analyzing the dataset and applying augmentations like cropping, flipping, and affine transformations. I first trained a simple Vanilla MLP, which quickly overfit and achieved limited accuracy, and then developed a deeper, regularized model with 16 hidden layers, batch normalization, and dropout, which improved generalization and reached about 51.6% test accuracy. I also compared the model’s predictions with human labels from the CIFAR‑10H dataset, finding that while the model sometimes agreed with humans (even when both were wrong), it also showed differences in perception.
My conclusion was that deeper architectures improve performance, but CIFAR‑10 remains a challenging dataset, and both humans and models struggle with the same difficult images.