🌟 Train Me 🌟

No-code Labeling and Training Toolkit for Computer Vision

With Improved Labelme for Image Labeling

TODO

This project is under development. Please consider everything here unstable. There are a lot of features need to be added in the future.

You can request new features through this contact form.

• Labeling: Integrate labelme
• Labeling: UI for textbox labeling (OCR, labels + positions)
• Labeling: Group objects (can be used in key-value matching problems)
• Labeling: Auto-labeling with YOLOv5
• Labeling: Tracking for video labeling
• Training: Project + Experiment management
• Training: object detection
• Training: image classification
• Training: image segmentation
• Training: instance segmentation
• Training: Add docker support for training
• Deployment: Export to ONNX
• Deployment: Export to TFLite
• Deployment: Export to TensorRT
• CI/CD for Pypi package publishment
• Unit tests
• Documentation

I. Install and run

• Requirements: Python >= 3.7

• Recommended: Miniconda/Anaconda https://docs.conda.io/en/latest/miniconda.html

• Create environment:

conda create -n trainme python=3.8
conda activate trainme

• (For macOS only) Install PyQt5 using Conda:

conda install -c conda-forge pyqt==5.15.7

• Install TrainMe:

pip install trainme-python

• Run app:

trainme_app

II. Development

• Generate resources:

pyrcc5 -o trainme/resources/resources.py trainme/resources/resources.qrc

• Run app:

python trainme/app.py

III. References

• labelme
• gpu_util
• Icons: Flat Icons