This repository is a compact PyTorch reimplementation of Siamese Networks for One-Shot Learning, trained and evaluated on the Omniglot dataset. It provides four core Python files: `model.py` defines a shared convolutional encoder with absolute difference scoring, `mydataset.py` handles in-memory image loading with rotation augmentations and positive/negative pair sampling, `train.py` orchestrates training and evaluation using gflags for configuration, and `make_dataset.py` flattens the Omniglot directory structure. The codebase uses PyTorch 1.0.1 and relies on deprecated APIs such as `torch.autograd.Variable`. Training achieves 89.5% accuracy on 20-way one-shot evaluation, slightly below the original paper's 92% due to optimizer choices (Adam vs SGD with momentum) and default parameter initialization. The repository also includes a static documentation site with bilingual (English/Chinese) support and a GitHub Pages deployment workflow. The research code is functional but tightly coupled to Omniglot and lacks modularity for reuse; the site is well-crafted but separate from the core ML logic.