mtcnn: self.thresholds, self.nms_thresholds,self.factor What does it mean? How do I tweak it? #122
Comments
|
have the same question about thresholds, what these 3 values mean? |
ipazc
pushed a commit
that referenced
this issue
Oct 7, 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.
Closed
ipazc
pushed a commit
that referenced
this issue
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.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
What is the meaning of these variables?
self.min_face_size = 20
self.thresholds = [0.5,0.5,0.5]
self.nms_thresholds = [0.6, 0.6, 0.6]
self.factor = 0.60
How do I tweak them?
What is the best mtcnn weights?
The text was updated successfully, but these errors were encountered: