process is killed automatically #125
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There is memory leak in this implementation. Refer this for the details. |
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ipazc
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…tch processing support - Completely refactored the MTCNN implementation following best coding practices. - Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22. - Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency. - Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110. - Added batch processing support to enhance performance for multiple input images. Fixes #9, #71. - Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116. - Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117. - Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23. - Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72. - Configured PyLint support to ensure code quality and style adherence. - Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity. - Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102. - Added a comprehensive training guide for the model. Fixes #35, #39. - Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73. - Configured GitHub Actions for continuous integration and delivery (CI/CD). - Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128. - Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95. - Added comprehensive unit tests for increased reliability and coverage.
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ipazc
pushed a commit
that referenced
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Oct 8, 2024
…tch processing support - Completely refactored the MTCNN implementation following best coding practices. - Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22. - Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency. - Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110. - Added batch processing support to enhance performance for multiple input images. Fixes #9, #71. - Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116. - Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117. - Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23. - Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72. - Configured PyLint support to ensure code quality and style adherence. - Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity. - Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102. - Added a comprehensive training guide for the model. Fixes #35, #39. - Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73. - Configured GitHub Actions for continuous integration and delivery (CI/CD). - Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128. - Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95. - Added comprehensive unit tests for increased reliability and coverage.
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When a large dataset is used for face detection in a loop, the process is killed automatically after a few times.
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