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DeepForest Changelog

Version 1.4.1 (Date: Oct 26, 2024)

  • Enhancement: Use GitHub Actions to publish the package.

Version 1.4.0 (Date: Oct 9, 2024)

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).
  • 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").

Version 1.3.3 (Date: Mar 12, 2024)

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

Version 1.3.0 (Date: Dec 3, 2023)

  • Deprecation: Removed IoU_Callback for better alignment with the PyTorch Lightning API (see issue).
  • 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.

Version 1.1.3 (Date: Nov 9, 2021)

  • Enhancement: Added box coordinates to the evaluation results frame for better result tracking.

Version 1.1.2 (Date: Sep 30, 2021)

  • Bug Fix: Fixed incorrect precision calculation in class_recall.csv when multiple classes were present.

Version 1.1.1 (Date: Sep 14, 2021)

  • Update: project_boxes now includes output options for both predict_tile and predict_image.
  • New Feature: Introduced annotations_to_shapefile, which reverses shapefile_to_annotations functionality. Thanks to @sdtaylor for this contribution.

Version 1.1.0 (Date: Aug 5, 2021)

  1. Enhancement: Empty frames are now allowed by passing annotations with 0's for all coordinates. Example format:

    image_path, 0, 0, 0, 0, "Tree"

  2. New Feature: Introduced check_release to reduce GitHub rate limit issues. When using use_release(), the local model will be used if check_release = False.

Version 1.0.9 (Date: Jul 14, 2021)

  • Enhancement: Improved default dtype for Windows users, thanks to @ElliotSalisbury for the contribution.

Version 1.0.0 (Date: Jun 7, 2021)

  • Major Update: Transitioned from TensorFlow to a PyTorch backend, enhancing performance and flexibility.

Version 0.1.34 (Date: )

  • Optimization: Profiled dataset and evaluation code, significantly improving evaluation performance.

Version 0.1.30 (Date: )

  • Bug Fix: Resolved issues to allow learning rate monitoring and decay based on val_classification_loss.