Transform model fine-tuning code to Python package #111
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Transform the model fine-tuning code in the Jupyter notebook files into a Python package named `deeptune` to help people fine-tune and train models of siamese architecture. * **Package Structure:** - Create `deeptune` package with subdirectories: `data`, `models`, `training`, `evaluation`, `utils`, and `cli`. - Add `__init__.py` files to each subdirectory to make them sub-packages. - Add `config.py` for configuration settings. - Add `requirements.txt` to list dependencies. * **CLI Support:** - Create `cli.py` in `cli` subdirectory to allow users to interact with the package through the command line. - Implement `evaluate_model` command to evaluate models. * **Loss Functions:** - Implement Triplet Loss, ArcFace Loss, and Contrastive Loss in `training/losses.py`. * **Data Generators:** - Implement `TripletGenerator` in `training/data_generators.py` to generate triplets for the triplet loss function. * **Evaluation:** - Move `Model-Training/eval-mark-I.py` to `evaluation/eval_mark_I.py` and update imports. - Move `Model-Training/Evaluate_model_1.ipynb` to `evaluation/evaluate_model_1.ipynb` and update imports. * **Documentation:** - Update `README.md` to include instructions for using the new Python package, CLI usage, and example code. --- For more details, open the [Copilot Workspace session](https://copilot-workspace.githubnext.com/Devasy23/FaceRec?shareId=XXXX-XXXX-XXXX-XXXX).
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Transform the model fine-tuning code in the Jupyter notebook files into a Python package named
deeptuneto help people fine-tune and train models of siamese architecture.Package Structure:
deeptunepackage with subdirectories:data,models,training,evaluation,utils, andcli.__init__.pyfiles to each subdirectory to make them sub-packages.config.pyfor configuration settings.requirements.txtto list dependencies.CLI Support:
cli.pyinclisubdirectory to allow users to interact with the package through the command line.evaluate_modelcommand to evaluate models.Loss Functions:
training/losses.py.Data Generators:
TripletGeneratorintraining/data_generators.pyto generate triplets for the triplet loss function.Evaluation:
Model-Training/eval-mark-I.pytoevaluation/eval_mark_I.pyand update imports.Model-Training/Evaluate_model_1.ipynbtoevaluation/evaluate_model_1.ipynband update imports.Documentation:
README.mdto include instructions for using the new Python package, CLI usage, and example code.For more details, open the Copilot Workspace session.