Mask_RCNN
Google Test
Mask_RCNN | Google Test | |
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28 | 67 | |
24,186 | 33,174 | |
0.5% | 1.9% | |
0.0 | 8.3 | |
10 days ago | 1 day ago | |
Python | C++ | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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Mask_RCNN
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Intuituvely Understanding Harris Corner Detector
The most widely used algorithms for classical feature detection today are "whatever opencv implements"
In terms of tech that's advancing at the moment? https://co-tracker.github.io/ if you want to track individual points, https://github.com/matterport/Mask_RCNN and its descendents if you want to detect, say, the cover of a book.
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Analyze defects and errors in the created images
Mask R-CNN
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List of AI-Models
Click to Learn more...
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Thought Dump About Recent AI Advancements And Palantir
- Mask RCNN https://github.com/matterport/Mask_RCNN (open source, so also not Palantir's)
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Why are python dependencies so broken?
pip install git+https://github.com/matterport/Mask_RCNN
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DeepCreamPy & Hent-AI Guide: Installation and anime censorship removal (Version 2)
It is important to realize that to do its masking procedures, Hent-AI uses the Mask RCNN (MRCNN) package from Matterport. The problem with this version of MRCNN is that it is not compatible with Tensorflow 2.X versions, essentially limiting Hent-AI compatibility to strict Tensorflow 1.X versions. Since Tensorflow 1.15 is the last of the Tensorflow 1.X versions and uses CUDA 10.0, which supports a maximum compute capability of 7.5, this means that the last NVIDIA GPU series that is compatible with the original Hent-AI implementation is the RTX 2000 series. This is, of course, not optimal since it means that RTX 3000 series and later GPUs cannot be used despite their significant computing power and high VRAM.
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[P] Mask R-CNN (matterport) does not generate masks or just generates them randomly
I read that it could bethe problem with scipy version (https://github.com/matterport/Mask_RCNN/issues/2122) so I downgraded it, I also tried to modify shift = np.array([0, 0, 1., 1.]) in utils.py but nothing helped.
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Mask RCNN importing error
I am assuming you did a pip install of this github repository, or did you run pip install mrcnn. The mrcnn package on pypi is just an example package and doesn't have any useful functionality. In addition, where did you get the code from that you are trying to run, from someone else or did you write it yourself? Reason I am asking is because the import error is to be expected since there indeed is no InferenceConfig class defined in mrcnn.visualize.
- Maskrcnn - Mask r-cnn for object detection and segmentation
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MRCNN TF==2.7.0
Hello AI learners, check out my own development of Mask-RCNN supporting Tensorflow2.7.0 and Keras2.8.0. This is an edit of MRCNN which supports Tensoflow1.0, only.
Google Test
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Creating k-NN with C++ (from Scratch)
cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) include(FetchContent) FetchContent_Declare( googletest GIT_REPOSITORY https://github.com/google/googletest.git GIT_TAG release-1.11.0 ) FetchContent_MakeAvailable(googletest) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() function(knn_cpp_test TEST_NAME TEST_SOURCE) add_executable(${TEST_NAME} ${TEST_SOURCE}) target_link_libraries(${TEST_NAME} PUBLIC matplot) aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/../lib LIB_SOURCES) target_link_libraries(${TEST_NAME} PRIVATE gtest gtest_main gmock gmock_main) target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/../) target_sources(${TEST_NAME} PRIVATE ${LIB_SOURCES} ) include(GoogleTest) gtest_discover_tests(${TEST_NAME}) endfunction() knn_cpp_test(LinearAlgebraTest la_test.cc) knn_cpp_test(KnnTest knn_test.cc) knn_cpp_test(UtilsTest utils_test.cc)
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Starting with C
Okay, time to start unit tests!!! We will use Unity Test Framework to do unit testing. It is one of widely used testing frameworks alongside with Check, Google Test etc. Just downloading source code, and putting it to the project folder is enough to make it work (that is also why it is portable).
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Just in case: Debian Bookworm comes with a buggy GCC
Updating GCC (it happened to GoogleTest).
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Automatically run tests, formatters & linters with CI!
Roy's project uses Google Test, a C++ testing framework. His testing setup is similar to mine as we both keep source files in one directory and tests in another. The key difference is that I can run the tests using the Visual Studios run button. It was fairly easy to write the new tests as there were existing ones that I could reference to check the syntax!
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C++ Unit Testing Using Google Test - My Experience
The Google Test Documentation provides a primer for first-time users. The primer introduces some basic concepts and terminology, some of which I've been able to learn for this lab exercise.
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Basic C++ Unit Testing with GTest, CMake, and Submodules
> git submodule add https://github.com/google/googletest.git > git submodule update --init --recursive
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VS code + cmake + gtest?
cmake_minimum_required(VERSION 3.14) project(my_project) # GoogleTest requires at least C++14 set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) include(FetchContent) FetchContent_Declare( googletest URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip ) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) enable_testing() add_executable( hello_test hello_test.cpp ) target_link_libraries( hello_test GTest::gtest_main ) include(GoogleTest) gtest_discover_tests(hello_test)
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FetchContent with Multiple URLs
FetchContent\_Declare(googletestGIT\_REPOSITORY [[email protected]](mailto:[email protected]):googletest.git [https://github.com/google/googletest.git](https://github.com/google/googletest.git)GIT\_TAG release-1.12.1)FetchContent\_MakeAvailable(googletest)
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CI/CD pipelines for embedded
Not sure about CppUnit but I can speak to my previous experience using the googletest framework which compiles your tests to an executable, and since it's a very simple framework we were able to cross-compile and run directly on our device. We just had to hook up a device to the server that was running the CI so it could flash it when needed. That basically meant that our process was:
- Basic CMake question regarding subdirectories
What are some alternatives?
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
Catch - A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
yolact - A simple, fully convolutional model for real-time instance segmentation.
Boost.Test - The reference C++ unit testing framework (TDD, xUnit, C++03/11/14/17)
mmdetection - OpenMMLab Detection Toolbox and Benchmark
CppUTest - CppUTest unit testing and mocking framework for C/C++
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
CppUnit - C++ port of JUnit
Mask-RCNN-training-with-docker-containers-on-Sagemaker
doctest - The fastest feature-rich C++11/14/17/20/23 single-header testing framework
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
Unity Test API - Simple Unit Testing for C