No-code Labeling and Training Toolkit for Computer Vision
With Improved Labelme for Image Labeling
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
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Requirements: Python >= 3.8
-
Recommended: Miniconda/Anaconda https://docs.conda.io/en/latest/miniconda.html
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Create environment:
conda create -n traincv python=3.8
conda activate traincv
- (For macOS only) Install PyQt5 using Conda:
conda install -c conda-forge pyqt==5.15.7
- Install traincv:
pip install traincv
- Run app:
traincv_app
Or
python -m traincv.app
- Generate resources:
pyrcc5 -o traincv/resources/resources.py traincv/resources/resources.qrc
- Run app:
python traincv/app.py
- labelme
- gpu_util
- Icons: Flat Icons