This Python-based Image Classification App allows you to label images for machine learning models. It supports various input such as folders and CSV file, shortcuts for labeling and provides progress files for saving progress.
- Light ImTagger is simple app for simple job
- Shortcuts Use ctrl+1-9 for fast labeling
- Folder Input: Load images directly from a specified folder.
- CSV Input: Use a CSV file to specify image paths and additional metadata.
- Progress Tracking: Keep track of your progress with a progress file.
- Autosave saving every changes into progress file automatically.
- AutoLabelling (Beta) Builitin support for auto lable prediction model running with transformers.
To install and set up the Image Classification App, follow these steps:
- Clone the repository:
git clone https://github.com/electro199/imTagger.git
cd imTagger
Install requirements
Python version 3.10 and 3.11 are recommended.
windows :
pip install -r requirements.txt
Unix :
pip3 install -r ./requirements.txt
This Feature is in beta to use in app set AUTO_LABELER_ENABLED
to True in main.py
and install dependency:
pip install transformers
On windows click run.bat
or in cmd run.bat
On unix in terminal python3 main.py
Pull requests are more than welcome! If you are planning to contribute a large patch, please create an issue first to get any upfront questions or design decisions out of the way first.