Install Mmcv-full

[Ophelia Gurin]

Donot install both versions in the same environment, otherwise you may encounter errors like ModuleNotFound. You need to uninstall one before installing the other. Installing the full version is highly recommended if CUDA is avaliable.

If you find that the above installation command does not use a pre-built package ending with .whl but a source package ending with .tar.gz, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv version, in which case you can build mmcv from source.

If you would like to use opencv-python-headless instead of opencv-python,e.g., in a minimum container environment or servers without GUI,you can first install it before installing MMCV to skip the installation of opencv-python.

If you do not find a corresponding version in the dropdown box above, you probably do not have a pre-built package corresponding to the PyTorch or CUDA or mmcv version, at which point you can build mmcv from source.

mmcv is only compiled on PyTorch 1.x.0 because the compatibilityusually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, youcan install mmcv compiled with PyTorch 1.x.0 and it usually works well.For example, if your PyTorch version is 1.8.1, you can feel free to choose 1.8.x.

If you find that the above installation command does not use a pre-built package ending with .whl but a source package ending with .tar.gz, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv-full version, in which case you can build mmcv-full from source.

If you do not find a corresponding version in the dropdown box above, you probably do not have a pre-built package corresponding to the PyTorch or CUDA or mmcv-full version, at which point you can build mmcv-full from source.

mmcv-full is only compiled on PyTorch 1.x.0 because the compatibilityusually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, youcan install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.For example, if your PyTorch version is 1.8.1, you can feel free to choose 1.8.x.

mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.

c. Some dependencies are optional. Simply running pip install -v -e . willonly install the minimum runtime requirements. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional]). Valid keys for the extras field are: all, tests, build, and optional.

d. If you would like to use albumentations, we suggest usingpip install albumentations>=0.3.2 --no-binary imgaug,albumentations. If you simply usepip install albumentations>=0.3.2, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). We should not allow opencv-python and opencv-python-headless installed at the same time, because it might cause unexpected issues. Please refer to official documentation for more details.

The required versions of MMCV, MMDetection and MMSegmentation for different versions of MMDetection3D are as below. Please install the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues.

The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

Following the above instructions, mmdetection is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).

If you want to input a ply file, you can use the following function and convert it to bin format. Then you can use the converted bin file to generate demo.Note that you need to install pandas and plyfile before using this script. This function can also be used for data preprocessing for training ply data.

I searched the error on google and they advise to install mmcv-full instead of mmcv but when I install the last version (1.7.1) of mmcv-full I have an error message telling me that I must have mmcv version 2.0

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Install from source (Recommended): You want to develop your own image classification task or new features based on MMClassification framework. For example, you want to add new dataset or new models. And you can use all tools we provided.

You will see the output result dict including pred_label, pred_score and pred_class in your terminal.And if you have graphical interface (instead of remote terminal etc.), you can enable --show option to showthe demo image with these predictions in a window.

For Windows users, ImageMagick will not be automatically detected by MoviePy,there is a need to modify moviepy/config_defaults.py file by providing the path to the ImageMagick binary called magick, like IMAGEMAGICK_BINARY = "C:\\Program Files\\ImageMagick_VERSION\\magick.exe"

Some dependencies are optional. Running python setup.py develop will only install the minimum runtime requirements.To use optional dependencies like decord, either install them with pip install -r requirements/optional.txtor specify desired extras when calling pip (e.g. pip install -v -e .[optional],valid keys for the [optional] field are all, tests, build, and optional) like pip install -v -e .[tests,build].

The git commit id will be written to the version number with step b, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.It is recommended that you run step b each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

Document detection is certainly a valid application for deep learning. If you need a place to get started, I came across this tutorial using PyTorch to extract information from images of student-id cards (link). It uses a Mask R-CNN model for detection and segmentation.

You can also train a Mask R-CNN model using the IceVision library (link), which uses the fastai training loop. I have worked with IceVision before for an object detection project, so let me know if you need help installing it.

IceVision includes parsers for COCO and Pascal VOC dataset formats out-of-the-box (link), so going with one of those would save you the extra work of making a custom parser (link). Double-check that the annotation tool you go with supports the formats you want for the applications (e.g., bounding boxes and masks).

Going by the CVAT GitHub project page, it does not appear that the tool supports masking for COCO format, only bounding boxes and points. It does list support for bounding boxes and segmentation masks for the PASCAL VOC format.

Going through the process with a test image, you export the bounding box annotations using the PASCAL VOC 1.1 option and the segmentation mask images using the Segmentation mask 1.1 option. You should be able to combine them into a single folder based on the layout of the VOC dataset used by IceVision.

Since you are following the Student ID tutorial, did you also switch to its annotation format for your dataset (i.e., a polygon with resolution-specific coordinates)? Do the annotations look as intended in the Visualize detection dataset section of the tutorial with your dataset?

b. If you would like to use albumentations, we suggest using pip install -r requirements/albu.txt or pip install -U albumentations --no-binary qudida,albumentations. If you simply use pip install albumentations==1.0.1, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). We recommended checking the environment after installing albumentation to ensure that opencv-python and opencv-python-headless are not installed at the same time, because it might cause unexpected issues if they both installed. Please refer to official documentation for more details.

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