Bugs

🔄 Normalization Layer Update : The VGG-Face model's normalization layer, causing issues for some GPU users (e.g. https://github.com/serengil/deepface/issues/957, https://github.com/serengil/deepface/issues/966), has been revamped. In this pull request, the normalization layer's functionality has been seamlessly integrated into the embedding calculation using regular NumPy operations instead of the Keras backend. PR-967

🚧 DeepID Model Representation Fix: Resolved an issue with the representation call of the DeepID model, ensuring smooth and error-free execution. PR-964

🐛 Ssd detector's alignment bug: When face detection is done with Ssd, unexpectedly it returns zero dimensional image. This causes errors in the alignment stage. We terminated the aligment procedure now if one of the dimension's shape of coming image is 0. PR-959

❗ Improved Dependency Handling: Meaningful exception messages are now thrown when importing uninstalled optional dependencies (e.g., Ssd, YuNet), providing clear guidance for resolving dependencies. PR-959

📦 Package Inclusion in Wheel Build: The readme and dependencies are now included in the built wheel package. This inclusion mitigates potential errors when uploading to Conda and ensures a smoother installation process for users. PR-950

Enhancements

🗄️ Datastore Management Enhancement: After running the find function, the created datastore is now expected to be updated rather than deleted if new instances are added in the database folder. This PR ensures that the pickle file is appropriately modified when instances are added, dropped, or if the content of existing images changes, improving overall data management. PR-959

📂 Modularized Functionality Logics: With this PR, the functionality logics for various operations in the DeepFace interface, including verify, find, analyze, represent, stream, and extract faces, have been relocated to the modules folder. As a result, the DeepFace interface (DeepFace.py) now boasts a cleaner and more organized structure. PR-953

🧬 Abstract Classes for Models: New abstract classes, namely FacialRecognition, Detector, and Demography, have been introduced. All facial recognition models, extended models (e.g., age, gender), and detectors now inherit from these abstract classes. [This architectural enhancement simplifies the process of adding new backends for these models. PR-960, PR-962

📚 Docstring Refinement: Docstrings in the deepface interface have been re-arranged for improved readability and understanding, enhancing the overall documentation. PR-965

🐳 Dockerfile Optimization: The Dockerfile has been updated to enhance the docker build cache layer, resulting in an optimized and more efficient build process. PR-951

Package

• https://pypi.org/project/deepface/0.0.82/