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LabelImg download for Linux

Free download LabelImg Linux app to run online in Ubuntu online, Fedora online or Debian online

This is the Linux app named LabelImg whose latest release can be downloaded as Binaryv1.8.1.zip. It can be run online in the free hosting provider OnWorks for workstations.

Download and run online this app named LabelImg with OnWorks for free.

Follow these instructions in order to run this app:

- 1. Downloaded this application in your PC.

- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 3. Upload this application in such filemanager.

- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.

- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 6. Download the application, install it and run it.





LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO and CreateML formats. Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended. Virtualenv can avoid a lot of the QT / Python version issues. Build and launch using the instructions. Click 'Change default saved annotation folder' in Menu/File. Click 'Open Dir'. Click 'Create RectBox'. Click and release left mouse to select a region to annotate the rect box. You can use right mouse to drag the rect box to copy or move it. The annotation will be saved to the folder you specify. You can refer to the hotkeys to speed up your workflow.


  • Your label list shall not change in the middle of processing a list of images
  • When you save an image, classes.txt will also get updated, while previous annotations will not be updated
  • You can edit the data/predefined_classes.txt to load pre-defined classes
  • When pressing space, the user can flag the image as verified, a green background will appear
  • The difficult field is set to 1 indicates that the object has been annotated as "difficult"
  • According to your deep neural network implementation, you can include or exclude difficult objects during training

Programming Language



Image Recognition

This is an application that can also be fetched from https://sourceforge.net/projects/labelimg.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.