Computer Vision Projects

Train a small convolutional neural network to classify handwritten digits end-to-end: download MNIST, build DataLoaders, define a CNN, train it on a GPU, and evaluate with accuracy and a confusion matrix. Variables persist across cells — download once, use everywhere.

A fast, fully in-memory project: load scikit-learn's 8x8 `digits` dataset, build a tiny multilayer perceptron in PyTorch, train it on the GPU, and evaluate. Great for seeing the full train/eval loop without any dataset download.

Classify 28x28 grayscale clothing images — T-shirts, trousers, sneakers and more — with a small CNN. Same end-to-end flow as MNIST, but the ten classes are real-world garments, so predictions read as human labels. Download once, train on a GPU, and evaluate.

Classify 28x28 grayscale images of cursive Japanese hiragana (10 classes) with a small CNN — a drop-in, slightly harder cousin of MNIST. Same end-to-end flow: download, build DataLoaders, train on a GPU, and evaluate with accuracy and a confusion matrix.

Train a small convolutional neural network to classify 32x32 color images across 10 classes (airplane, automobile, bird, cat, ...). Download CIFAR-10, build DataLoaders, define a CNN with batch-norm, train it on a GPU, and evaluate with accuracy and a confusion matrix. Variables persist across cells — download once, use everywhere.

A compact face-recognition project on scikit-learn's Olivetti dataset: 40 people, 400 64x64 grayscale photos. Load it in memory, build a small CNN, train it on a GPU, and evaluate — no large download, sklearn fetches the data for you.