Build and train computer vision models with 5 lines of code
Detecto is a Python package for quick and easy object detection. Below are just a few of the features available:
- Train models on custom datasets
- Get all or top predictions on an image
- Run object detection on videos
- Save and load models from files
Detecto is built on top of PyTorch, meaning models trained with Detecto can easily be extracted and used with PyTorch code.
Usage and Docs
To install Detecto using pip, run the following command:
pip3 install detecto
After installing Detecto, you can train a machine learning model on a custom dataset and run object detection on a video with under ten lines of code:
from detecto.core import Model, Dataset, DataLoader from detecto.utils import xml_to_csv from detecto.visualize import detect_video xml_to_csv('xml_labels/', 'labels.csv') dataset = Dataset('labels.csv', 'images/') loader = DataLoader(dataset) model = Model(['dog', 'cat', 'rabbit']) model.fit(loader) detect_video(model, 'input_video.mp4', 'output_video.avi')
Alternatively, check out the demo on Colab.
All issues and pull requests are welcome! To run the code locally, first fork the repository and then run the following commands on your computer:
git clone https://github.com/<your-username>/detecto.git cd detecto # Recommended to create a virtual environment before the next step pip3 install -r requirements.txt
When adding code, be sure to write unit tests and docstrings where necessary.
Tests are located in
detecto/tests and can be run using pytest:
python3 -m pytest
To generate the documentation locally, run the following commands:
cd docs make html
The documentation can then be viewed at
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