symbrain_siamese

Compact python notebooks and utilities for experimenting with Siamese neural networks and similarity learning. The repository is primarily Jupyter Notebook–driven for quick iteration, visualization, and experimentation.

• Repo: toelt-llc/symbrain_siamese

What’s inside

• Jupyter notebooks for data exploration, model prototyping, and evaluation
• Lightweight Python helpers (e.g., data loaders, preprocessing, training/eval routines)
• Example workflows for building and testing Siamese-style models (e.g., verification, metric learning)

Quick start

1. Clone the repo:

   git clone https://github.com/toelt-llc/symbrain_siamese.git
   cd symbrain_siamese
   

2. Set up an environment (venv or conda) and install dependencies:

   • If a requirements.txt or environment.yml is present:

     pip install -r requirements.txt
     # or: conda env create -f environment.yml && conda activate <env-name>
     

   • Otherwise, install common notebook/ML packages you use (e.g., jupyter, numpy, pandas, matplotlib, and a deep learning framework like PyTorch or TensorFlow).

3. Launch notebooks:

   jupyter lab
   # or: jupyter notebook
   

   Open the notebooks and adjust any config (paths, hyperparameters) at the top of each file.

Data

• Provide your dataset paths in the notebooks (or configuration cells).
• For Siamese training, ensure you can generate pairs/triplets (positive/negative) or adapt the provided helper code to your data format.

Results and logs

• Notebooks will typically save artifacts (models, metrics, plots) to a local folder (e.g., outputs/); adjust paths as needed in the notebook cells.

Contributing

• Keep notebooks concise and documented (short cell comments, clear section headers).
• Prefer small, composable Python helpers over large monolithic scripts.
• Open an issue or PR for substantial changes.

License

No license is currently specified by the repository. If you plan to use or distribute this work, add an appropriate license file.