Deepforest

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The major innovations are:

1. Improve Tests on edge cases

Additional features and enhancements include:

- **Documentation:** Improved documentation workflow and version available

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The major innovations are:

1. Restructured package layout by moving code into src/ directory for better organization
2. Added batch prediction capabilities for improved processing of multiple images
3. Implemented support for image dataframes to allow more flexible input formats
4. Created plot_annotations method for better visualization of predictions
5. Added out-of-memory dataset sample for handling large datasets efficiently

Additional features and enhancements include:

- **Enhancement:** Reorganized package structure to follow modern Python packaging standards
- **Enhancement:** Enhanced test coverage for new features
- **Enhancement:** Improved code organization and maintainability
- **Documentation:** Added docformatter for consistent docstring formatting
- **Documentation:** Improved documentation clarity and organization
- **Documentation:** Added new contributor: Dingyi Fang

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- **Enhancement:** Use GitHub Actions to publish the package.

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The major innovations are

1. New model loading framework using HuggingFace. DeepForest models are now available on https://huggingface.co/weecology. The models can be loaded using load_model() and used for inference.
2. An all purpose read_file method is introduced to read annotations from various formats including CSV, JSON, and Pascal VOC.
3. The CropModel class is introduced to classify detected objects using a trained classification model. Use when a multi-class DeepForest model is not sufficiently flexible, such as when new data sources are used for fine-grained classification and class imbalance.
4. deepforest.visualize.plot_results is now the primary method for visualizing predictions. The function is more flexible and allows for customizing the plot using supervision package.

Additional features and enhancements include:

- **New Feature:** A crop_raster function is introduced to crop a raster image using a bounding box.
- **New Feature:** Added beta support for multiple annotation types including point, box, and polygon.
- **New Feature:** Added support for learning rates scheduling using the ``torch.optim.lr_scheduler`` module. The learning rate scheduler can be specified in the configuration file.
- **New Utility:** Created ``utilities.download_ArcGIS_REST`` function to download tiles from the ArcGIS REST API (e.g., NAIP imagery).

- **Enhancement:** The training module better matches torchvision negative anchors format for empty frames.

- **Deprecation:** ``shapefile_to_annotations`` in ``deepforest/utilities.py`` is deprecated in favor of the more general ``read_file`` method.
- **Deprecation:** ``predict`` in ``deepforest/main.py``. The ``return_plot`` argument is deprecated and will be removed in version 2.0. Use ``visualize.plot_results`` instead.
- **Deprecation:** ``predict_tile`` in ``deepforest/main.py``. Deprecated arguments ``return_plot``, ``color``, and ``thickness`` will be removed in version 2.0.
- **Deprecation:** ``crop_function`` in ``deepforest/preprocess.py``. The ``base_dir`` argument is deprecated and will be removed in version 2.0. Use ``save_dir`` instead.
- **Deprecation:** The deepforest.visualize. ``IoU_Callback`` for better alignment with the PyTorch Lightning API (see `issue <https://github.com/Lightning-AI/pytorch-lightning/issues/19101>`_).
- **Deprecation:** ``deepforest.main.use_release`` and ``deepforest.main.use_bird_release`` are deprecated in favor of the new model loading framework, for example using deepforest.main.load_model("weecology/deepforest-bird").

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- **Enhancement:** ``split_raster`` now allows ``annotations_file`` to be ``None``, enabling flexibility during data preprocessing.

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- **Deprecation:** Removed ``IoU_Callback`` for better alignment with the PyTorch Lightning API (see `issue <https://github.com/Lightning-AI/pytorch-lightning/issues/19101>`_).
- **Refactor:** Evaluation code now leverages the PyTorch Lightning evaluation loop for result calculation during training.
- **Refactor:** Simplified ``image_callbacks`` by using predictions directly. No need to specify the root directory or CSV file, as the evaluation file is assumed.