SeekDeeper: Minimal Implementations of Popular AI Models

[📖中文ReadMe]

Motivation

Official code repositories often include extensive engineering details, which may increase the learning burden for beginners. This repository aims to provide minimal PyTorch implementations of representative models, thereby facilitating conceptual understanding and experimental reproduction. In addition, many tutorials focus primarily on model architectures while omitting the complete workflow of data loading, preprocessing, training, and evaluation. Therefore, each model implementation is designed to expose relevant training frameworks, configuration utilities, and data-loading pipelines, so that learners can study the full experimental process in a coherent manner.

Models

Directory Structure

For each model, the typical directory structure is as follows:

<model name>/
├── checkpoints/
├── modules/
├── datasets/
├── assets/
├── README.md
├── data.py
├── config.py
├── train.ipynb
└── inference.ipynb

• checkpoints/: Contains pre-trained model weights for direct use in inference.ipynb. Sometimes, pre-trained parameters from official repositories are loaded directly.
• modules/: Contains modules necessary for model implementation.
• datasets/: Contains datasets required for training or inference validation, which may sometimes be downloaded to this directory via code.
• assets/: Contains original papers and images used by the model README.
• README.md: Introduces the implemented task and describes the implementation details.
• data.py: Defines Dataset, Dataloader, or data preprocessing.
• config.py: Defines hyperparameters needed for the experiment.
• train.ipynb: Clearly presents the process from data loading, preprocessing, to training and evaluation.
• inference.ipynb: Loads model parameters from the checkpoints/ directory for inference.

License

This project uses the MIT License.