CV-Studio is a graphical annotation tool to address different Computer Vision tasks.
CV-Studio is developed in Python, Qt, SQLite and uses PyTorch's resources to train deep learning models.
CVStudio supports:
Datasets:
- Create and manage your datasets for images.
- Manually annotate images:
- Using a label system for classification problems.
- Using a bounding box for localization and object detection problems.
- Using a polygon tool or freehand selection for segmentation tasks.
- Auto-annotate images with a pretrained model to continue tagging the images by your own.
- Datasets: Annotations for videos.
- Models:
- Build your own custom models using a pretrained model from PyTorch Hub and your annotated dataset.
- Publish your own custom models to PyTorch Hub.
- Experiments: Develop experiments using your datasets and models from PyTorch Hub or your custom trained models.
- Platforms: macOS and Linux support.
Note: CV-Studio only have been developed and tested on Windows. Future platforms (macOS and Linux) are in the roadmap.
Windows + Anaconda:
- Clone the repository:
git clone https://github.com/haruiz/CvStudio.git
- Download and install Anaconda (Python 3+).
- Open Anaconda Prompt, go to CvStudio directory and follow the next steps:
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Create a new environment with Python 3.6:
conda create --name cvstudio python=3.6
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Install required libraries:
pip install -r pip_req.txt
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Install PyTorch with conda following the instructions in the official site. For the purpose of this tutorial we are selecting the following configuration:
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch-nightly
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- Run and enjoy CV-Studio Studio:
python cvstudio.py
Check out the wiki.
Send a pull request.
Citation: haruiz. CV-Studio. Git code (2019). https://github.com/haruiz/CvStudio
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