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threshold #57

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ftfajardo opened this issue Oct 7, 2019 · 1 comment
Open

threshold #57

ftfajardo opened this issue Oct 7, 2019 · 1 comment

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@ftfajardo
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i am using this in images, what is the point of the threshold value? my results are the same with 0.1 and 0.9?

@ipazc
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ipazc commented Oct 14, 2019

The steps_threshold is a list of thresholds used for each stage respectively. If you play with the numbers, you can alter the face detection rate.
You should adapt these values upon your dataset.

By default the values are: [0.6, 0.7, 0.7]

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.
@ipazc ipazc mentioned this issue Oct 8, 2024
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.
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