BBox-Label-Tool | A simple tool for labeling object bounding boxes in images | Data Labeling library

 by   puzzledqs Python Version: Current License: MIT

kandi X-RAY | BBox-Label-Tool Summary

kandi X-RAY | BBox-Label-Tool Summary

BBox-Label-Tool is a Python library typically used in Artificial Intelligence, Data Labeling applications. BBox-Label-Tool has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However BBox-Label-Tool build file is not available. You can download it from GitHub.

A simple tool for labeling object bounding boxes in images, implemented with Python Tkinter. Updates: - 2017.5.21 Check out the ``multi-class`` branch for a multi-class version implemented by @jxgu1016.

            kandi-support Support

              BBox-Label-Tool has a medium active ecosystem.
              It has 1102 star(s) with 568 fork(s). There are 45 watchers for this library.
              It had no major release in the last 6 months.
              There are 24 open issues and 12 have been closed. On average issues are closed in 35 days. There are 16 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of BBox-Label-Tool is current.

            kandi-Quality Quality

              BBox-Label-Tool has 0 bugs and 43 code smells.

            kandi-Security Security

              BBox-Label-Tool has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              BBox-Label-Tool code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              BBox-Label-Tool is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              BBox-Label-Tool releases are not available. You will need to build from source code and install.
              BBox-Label-Tool has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              BBox-Label-Tool saves you 86 person hours of effort in developing the same functionality from scratch.
              It has 221 lines of code, 12 functions and 1 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed BBox-Label-Tool and discovered the below as its top functions. This is intended to give you an instant insight into BBox-Label-Tool implemented functionality, and help decide if they suit your requirements.
            • Initialize the tool .
            • Initialize the dir for the specified directory
            • Load the image
            • Handle mousemove .
            • Mouse click .
            • Delete the bbox from the listbox
            • Save the image
            • Cancel bbox .
            • Clear the bbox .
            • Go to next image .
            Get all kandi verified functions for this library.

            BBox-Label-Tool Key Features

            No Key Features are available at this moment for BBox-Label-Tool.

            BBox-Label-Tool Examples and Code Snippets

            Yolo-Android,Training Custom Dataset By Using Darknet
            Javadot img1Lines of Code : 16dot img1no licencesLicense : No License
            copy iconCopy
            Note-1: You must use cudnn 9.0 version. So, you change "9.1" to "9.0" in darknet.sln to build your project. 
            Note-2: You must use opencv 3.4.0. and you put "opencv_ffmpeg340_64.dll" and "opencv_world340.dll" files next to darknet.exe file.
             1- Crea  
            1. 背景
            Pythondot img2Lines of Code : 10dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            4.000000	3.000000	4.000000	3.000000	1.000000	coat/657i67i6k_1.jpg
            1st 4.000000 means in OptionsClothesStyle attribute is "small suit"/"小西装"
            2nd 3.000000 means in OptionsClothesColor attribute is "gray"/"灰色"
            3rd 4.000000 means in OptionsCClothesTextu  

            Community Discussions


            YOLO : Either overfits or underfits, increase batch or increase sample image pool?
            Asked 2019-Sep-11 at 01:30

            I'm trying to train my yolo model to identify fire extinguishers and to label it as "Fire Safety". Currently is either I get a overfit or underfit images(see below).

            My sample images size with annotations is around ~1500

            yolo-new.cfg config of width=608 and height=608

            And I have trained using the following command:

            python flow --model cfg/yolo-new.cfg --labels one_label.txt --train --trainer adam --dataset "C://Users//G//Desktop//Development//ML//YOLO//BBox-Label-Tool//Images//002" --annotation "C://Users//G//Desktop//Development//ML//YOLO//BBox-Label-Tool//AnnotationsXML//002" --batch 4 --gpu 0.8

            So after 13000 steps:

            So I went to validate my results and this is what I get(Checkpoint 13000):

            So perhaps I thought this might be a case of severe overfitting, thus I iterate through the checkpoints to see which has the closest fit.

            This is what I get using checkpoint 6500

            This is what I get using checkpoint 6000

            This is what I get using checkpoint 5500

            So, as you can see checkpoint 6000 is the best possible result in my case but it isn't good enough. How do I improve on this? Increase batch size ?(My GPU 1070Ti cant handle. Cuda out of memory occurs) Any Ideas to solve this?



            Answered 2019-Sep-11 at 01:30

            Using Yolov3 to train my imageset solved my issue.

            One thing to note is not to leave any blanks during annotation, perhaps this might be one of the reasons why the detection did not work as planned.



            BBox labeling tool
            Asked 2018-Oct-24 at 06:39

            I am trying to train YOLOv2 on custom images and am following this link

            For annotating the objects I am using the BBox Labeling Tool. The above link asked me to change the directory in line 126 in the file in BBox Labeling Tool folder. After I changed the line, the code looks like this.



            Answered 2018-Oct-24 at 06:39

            From what I understood this is supposed to be the path to the tool itself.


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


            No vulnerabilities reported

            Install BBox-Label-Tool

            You can download it from GitHub.
            You can use BBox-Label-Tool like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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