deeplabel | A cross-platform image annotation tool for machine learning | Data Labeling library

by jveitchmichaelis C++ Version: v0.15 License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | deeplabel Summary

deeplabel is a C++ library typically used in Artificial Intelligence, Data Labeling, Deep Learning applications. deeplabel has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Download the latest release!. DeepLabel is a cross-platform tool for annotating images with labelled bounding boxes. A typical use-case for the program is labelling ground truth data for object-detection machine learning applications. DeepLabel runs as a standalone app and compiles on Windows, Linux and Mac. Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model. Ready made binaries for Windows and OS X are on the release page. It is recommended that you build for Linux yourself. DeepLabel was built with convenience in mind. Image locations, classes and labels are stored in a local sqlite database (called a project, in the application). When a label is added or removed, this is immediately reflected in the database.

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Support

deeplabel has a low active ecosystem.

It has 122 star(s) with 16 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 16 have been closed. On average issues are closed in 13 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deeplabel is v0.15

Quality

deeplabel has 0 bugs and 0 code smells.

Security

deeplabel has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deeplabel code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deeplabel does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

deeplabel releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 47 lines of code, 2 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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deeplabel Key Features

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deeplabel Examples and Code Snippets

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Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

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ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

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ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

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ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deeplabel

It's recommended that you use Qt5, but Qt4 will probably work. You need to have Qt's SQL extensions installed. This is mostly a pure Qt project, but there are some limitations to what Qt can do with images. In particular, scaling sucks (even with Qt::SmoothTransform). Qt's image reader is also not particularly robust, so OpenCV is used there. OpenCV is also used for image augmentation. On OS X or Linux it's expected that you have pkg-config installed to handle dependencies. You need to compile OpenCV with contrib (-DOPENCV_EXTRA_MODULES_PATH) for object tracking. You should also compile with (-DOPENCV_GENERATE_PKGCONFIG). Only OpenCV 4+ is supported due to API changes. Make sure you checkout the same tagged release for the main repository and the contrib repository. On Mac, Homebrew automatically include pkg-config support and the contrib packages. Build opencv using your preferred method. You need Qt5 installed - not just Qt Creator.

Support

You can load in a Darknet-type model, for example Yolov3, Yolov3-spp and Yolov3-Tiny or a Tensorflow model. Most of the standard Tensorflow models are supported e.g. Faster-RCNN, Mobilenet-SSD. Currently the assumption is that the model ends with a DetectionOutput layer. This is an Nx7 sized tensor where N is the number of output labels, after non-maximum suppression has been applied (typically 100 for stock models). The columns are batch id, class id, confidence, box coords.

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